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class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.05697">arXiv:2411.05697</a> <span> [<a href="https://arxiv.org/pdf/2411.05697">pdf</a>, <a href="https://arxiv.org/format/2411.05697">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> IPMN Risk Assessment under Federated Learning Paradigm </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Pan%2C+H">Hongyi Pan</a>, <a href="/search/?searchtype=author&query=Hong%2C+Z">Ziliang Hong</a>, <a href="/search/?searchtype=author&query=Durak%2C+G">Gorkem Durak</a>, <a href="/search/?searchtype=author&query=Keles%2C+E">Elif Keles</a>, <a href="/search/?searchtype=author&query=Aktas%2C+H+E">Halil Ertugrul Aktas</a>, <a href="/search/?searchtype=author&query=Taktak%2C+Y">Yavuz Taktak</a>, <a href="/search/?searchtype=author&query=Medetalibeyoglu%2C+A">Alpay Medetalibeyoglu</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Z">Zheyuan Zhang</a>, <a href="/search/?searchtype=author&query=Velichko%2C+Y">Yury Velichko</a>, <a href="/search/?searchtype=author&query=Spampinato%2C+C">Concetto Spampinato</a>, <a href="/search/?searchtype=author&query=Schoots%2C+I">Ivo Schoots</a>, <a href="/search/?searchtype=author&query=Bruno%2C+M+J">Marco J. Bruno</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Pallavi Tiwari</a>, <a href="/search/?searchtype=author&query=Bolan%2C+C">Candice Bolan</a>, <a href="/search/?searchtype=author&query=Gonda%2C+T">Tamas Gonda</a>, <a href="/search/?searchtype=author&query=Miller%2C+F">Frank Miller</a>, <a href="/search/?searchtype=author&query=Keswani%2C+R+N">Rajesh N. Keswani</a>, <a href="/search/?searchtype=author&query=Wallace%2C+M+B">Michael B. Wallace</a>, <a href="/search/?searchtype=author&query=Xu%2C+Z">Ziyue Xu</a>, <a href="/search/?searchtype=author&query=Bagci%2C+U">Ulas Bagci</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.05697v1-abstract-short" style="display: inline;"> Accurate classification of Intraductal Papillary Mucinous Neoplasms (IPMN) is essential for identifying high-risk cases that require timely intervention. In this study, we develop a federated learning framework for multi-center IPMN classification utilizing a comprehensive pancreas MRI dataset. This dataset includes 653 T1-weighted and 656 T2-weighted MRI images, accompanied by corresponding IPMN… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.05697v1-abstract-full').style.display = 'inline'; document.getElementById('2411.05697v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.05697v1-abstract-full" style="display: none;"> Accurate classification of Intraductal Papillary Mucinous Neoplasms (IPMN) is essential for identifying high-risk cases that require timely intervention. In this study, we develop a federated learning framework for multi-center IPMN classification utilizing a comprehensive pancreas MRI dataset. This dataset includes 653 T1-weighted and 656 T2-weighted MRI images, accompanied by corresponding IPMN risk scores from 7 leading medical institutions, making it the largest and most diverse dataset for IPMN classification to date. We assess the performance of DenseNet-121 in both centralized and federated settings for training on distributed data. Our results demonstrate that the federated learning approach achieves high classification accuracy comparable to centralized learning while ensuring data privacy across institutions. This work marks a significant advancement in collaborative IPMN classification, facilitating secure and high-accuracy model training across multiple centers. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.05697v1-abstract-full').style.display = 'none'; document.getElementById('2411.05697v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.01390">arXiv:2411.01390</a> <span> [<a href="https://arxiv.org/pdf/2411.01390">pdf</a>, <a href="https://arxiv.org/format/2411.01390">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> A New Logic For Pediatric Brain Tumor Segmentation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Bengtsson%2C+M">Max Bengtsson</a>, <a href="/search/?searchtype=author&query=Keles%2C+E">Elif Keles</a>, <a href="/search/?searchtype=author&query=Durak%2C+G">Gorkem Durak</a>, <a href="/search/?searchtype=author&query=Anwar%2C+S">Syed Anwar</a>, <a href="/search/?searchtype=author&query=Velichko%2C+Y+S">Yuri S. Velichko</a>, <a href="/search/?searchtype=author&query=Linguraru%2C+M+G">Marius G. Linguraru</a>, <a href="/search/?searchtype=author&query=Waanders%2C+A+J">Angela J. Waanders</a>, <a href="/search/?searchtype=author&query=Bagci%2C+U">Ulas Bagci</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.01390v1-abstract-short" style="display: inline;"> In this paper, we present a novel approach for segmenting pediatric brain tumors using a deep learning architecture, inspired by expert radiologists' segmentation strategies. Our model delineates four distinct tumor labels and is benchmarked on a held-out PED BraTS 2024 test set (i.e., pediatric brain tumor datasets introduced by BraTS). Furthermore, we evaluate our model's performance against the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.01390v1-abstract-full').style.display = 'inline'; document.getElementById('2411.01390v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.01390v1-abstract-full" style="display: none;"> In this paper, we present a novel approach for segmenting pediatric brain tumors using a deep learning architecture, inspired by expert radiologists' segmentation strategies. Our model delineates four distinct tumor labels and is benchmarked on a held-out PED BraTS 2024 test set (i.e., pediatric brain tumor datasets introduced by BraTS). Furthermore, we evaluate our model's performance against the state-of-the-art (SOTA) model using a new external dataset of 30 patients from CBTN (Children's Brain Tumor Network), labeled in accordance with the PED BraTS 2024 guidelines. We compare segmentation outcomes with the winning algorithm from the PED BraTS 2023 challenge as the SOTA model. Our proposed algorithm achieved an average Dice score of 0.642 and an HD95 of 73.0 mm on the CBTN test data, outperforming the SOTA model, which achieved a Dice score of 0.626 and an HD95 of 84.0 mm. Our results indicate that the proposed model is a step towards providing more accurate segmentation for pediatric brain tumors, which is essential for evaluating therapy response and monitoring patient progress. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.01390v1-abstract-full').style.display = 'none'; document.getElementById('2411.01390v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.22530">arXiv:2410.22530</a> <span> [<a href="https://arxiv.org/pdf/2410.22530">pdf</a>, <a href="https://arxiv.org/format/2410.22530">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> Adaptive Aggregation Weights for Federated Segmentation of Pancreas MRI </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Pan%2C+H">Hongyi Pan</a>, <a href="/search/?searchtype=author&query=Durak%2C+G">Gorkem Durak</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Z">Zheyuan Zhang</a>, <a href="/search/?searchtype=author&query=Taktak%2C+Y">Yavuz Taktak</a>, <a href="/search/?searchtype=author&query=Keles%2C+E">Elif Keles</a>, <a href="/search/?searchtype=author&query=Aktas%2C+H+E">Halil Ertugrul Aktas</a>, <a href="/search/?searchtype=author&query=Medetalibeyoglu%2C+A">Alpay Medetalibeyoglu</a>, <a href="/search/?searchtype=author&query=Velichko%2C+Y">Yury Velichko</a>, <a href="/search/?searchtype=author&query=Spampinato%2C+C">Concetto Spampinato</a>, <a href="/search/?searchtype=author&query=Schoots%2C+I">Ivo Schoots</a>, <a href="/search/?searchtype=author&query=Bruno%2C+M+J">Marco J. Bruno</a>, <a href="/search/?searchtype=author&query=Keswani%2C+R+N">Rajesh N. Keswani</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Pallavi Tiwari</a>, <a href="/search/?searchtype=author&query=Bolan%2C+C">Candice Bolan</a>, <a href="/search/?searchtype=author&query=Gonda%2C+T">Tamas Gonda</a>, <a href="/search/?searchtype=author&query=Goggins%2C+M+G">Michael G. Goggins</a>, <a href="/search/?searchtype=author&query=Wallace%2C+M+B">Michael B. Wallace</a>, <a href="/search/?searchtype=author&query=Xu%2C+Z">Ziyue Xu</a>, <a href="/search/?searchtype=author&query=Bagci%2C+U">Ulas Bagci</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.22530v2-abstract-short" style="display: inline;"> Federated learning (FL) enables collaborative model training across institutions without sharing sensitive data, making it an attractive solution for medical imaging tasks. However, traditional FL methods, such as Federated Averaging (FedAvg), face difficulties in generalizing across domains due to variations in imaging protocols and patient demographics across institutions. This challenge is part… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.22530v2-abstract-full').style.display = 'inline'; document.getElementById('2410.22530v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.22530v2-abstract-full" style="display: none;"> Federated learning (FL) enables collaborative model training across institutions without sharing sensitive data, making it an attractive solution for medical imaging tasks. However, traditional FL methods, such as Federated Averaging (FedAvg), face difficulties in generalizing across domains due to variations in imaging protocols and patient demographics across institutions. This challenge is particularly evident in pancreas MRI segmentation, where anatomical variability and imaging artifacts significantly impact performance. In this paper, we conduct a comprehensive evaluation of FL algorithms for pancreas MRI segmentation and introduce a novel approach that incorporates adaptive aggregation weights. By dynamically adjusting the contribution of each client during model aggregation, our method accounts for domain-specific differences and improves generalization across heterogeneous datasets. Experimental results demonstrate that our approach enhances segmentation accuracy and reduces the impact of domain shift compared to conventional FL methods while maintaining privacy-preserving capabilities. Significant performance improvements are observed across multiple hospitals (centers). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.22530v2-abstract-full').style.display = 'none'; document.getElementById('2410.22530v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 31 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 29 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.16296">arXiv:2410.16296</a> <span> [<a href="https://arxiv.org/pdf/2410.16296">pdf</a>, <a href="https://arxiv.org/format/2410.16296">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> CirrMRI600+: Large Scale MRI Collection and Segmentation of Cirrhotic Liver </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Jha%2C+D">Debesh Jha</a>, <a href="/search/?searchtype=author&query=Susladkar%2C+O+K">Onkar Kishor Susladkar</a>, <a href="/search/?searchtype=author&query=Gorade%2C+V">Vandan Gorade</a>, <a href="/search/?searchtype=author&query=Keles%2C+E">Elif Keles</a>, <a href="/search/?searchtype=author&query=Antalek%2C+M">Matthew Antalek</a>, <a href="/search/?searchtype=author&query=Seyithanoglu%2C+D">Deniz Seyithanoglu</a>, <a href="/search/?searchtype=author&query=Cebeci%2C+T">Timurhan Cebeci</a>, <a href="/search/?searchtype=author&query=Aktas%2C+H+E">Halil Ertugrul Aktas</a>, <a href="/search/?searchtype=author&query=Kartal%2C+G+D">Gulbiz Dagoglu Kartal</a>, <a href="/search/?searchtype=author&query=Kaymakoglu%2C+S">Sabahattin Kaymakoglu</a>, <a href="/search/?searchtype=author&query=Erturk%2C+S+M">Sukru Mehmet Erturk</a>, <a href="/search/?searchtype=author&query=Velichko%2C+Y">Yuri Velichko</a>, <a href="/search/?searchtype=author&query=Ladner%2C+D">Daniela Ladner</a>, <a href="/search/?searchtype=author&query=Borhani%2C+A+A">Amir A. Borhani</a>, <a href="/search/?searchtype=author&query=Medetalibeyoglu%2C+A">Alpay Medetalibeyoglu</a>, <a href="/search/?searchtype=author&query=Durak%2C+G">Gorkem Durak</a>, <a href="/search/?searchtype=author&query=Bagci%2C+U">Ulas Bagci</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.16296v1-abstract-short" style="display: inline;"> Liver cirrhosis, the end stage of chronic liver disease, is characterized by extensive bridging fibrosis and nodular regeneration, leading to an increased risk of liver failure, complications of portal hypertension, malignancy and death. Early diagnosis and management of end-stage cirrhosis are significant clinical challenges. Magnetic resonance imaging (MRI) is a widely available, non-invasive im… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.16296v1-abstract-full').style.display = 'inline'; document.getElementById('2410.16296v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.16296v1-abstract-full" style="display: none;"> Liver cirrhosis, the end stage of chronic liver disease, is characterized by extensive bridging fibrosis and nodular regeneration, leading to an increased risk of liver failure, complications of portal hypertension, malignancy and death. Early diagnosis and management of end-stage cirrhosis are significant clinical challenges. Magnetic resonance imaging (MRI) is a widely available, non-invasive imaging technique for cirrhosis assessment. However, the stage of liver fibrosis cannot be easily differentiated. Moreover, the fibrotic liver tissue (cirrhotic liver) causes significant change in liver enhancement, morphology and signal characteristics, which poses substantial challenges for the development of computer-aided diagnostic applications. Deep learning (DL) offers a promising solution for automatically segmenting and recognizing cirrhotic livers in MRI scans, potentially enabling fibrosis stage classification. However, the lack of datasets specifically focused on cirrhotic livers has hindered progress. CirrMRI600+ addresses this critical gap. This extensive dataset, the first of its kind, comprises 628 high-resolution abdominal MRI scans (310 T1-weighted and 318 T2-weighted, totaling nearly 40,000 slices) with annotated segmentation labels for cirrhotic livers. Unlike previous datasets, CirrMRI600+ specifically focuses on cirrhotic livers, capturing the complexities of this disease state. The link to the dataset is made publicly available at: https://osf.io/cuk24/. We also share 11 baseline deep learning segmentation methods used in our rigorous benchmarking experiments: https://github.com/NUBagciLab/CirrMRI600Plus. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.16296v1-abstract-full').style.display = 'none'; document.getElementById('2410.16296v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.04491">arXiv:2408.04491</a> <span> [<a href="https://arxiv.org/pdf/2408.04491">pdf</a>, <a href="https://arxiv.org/format/2408.04491">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Towards Synergistic Deep Learning Models for Volumetric Cirrhotic Liver Segmentation in MRIs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Gorade%2C+V">Vandan Gorade</a>, <a href="/search/?searchtype=author&query=Susladkar%2C+O">Onkar Susladkar</a>, <a href="/search/?searchtype=author&query=Durak%2C+G">Gorkem Durak</a>, <a href="/search/?searchtype=author&query=Keles%2C+E">Elif Keles</a>, <a href="/search/?searchtype=author&query=Aktas%2C+E">Ertugrul Aktas</a>, <a href="/search/?searchtype=author&query=Cebeci%2C+T">Timurhan Cebeci</a>, <a href="/search/?searchtype=author&query=Medetalibeyoglu%2C+A">Alpay Medetalibeyoglu</a>, <a href="/search/?searchtype=author&query=Ladner%2C+D">Daniela Ladner</a>, <a href="/search/?searchtype=author&query=Jha%2C+D">Debesh Jha</a>, <a href="/search/?searchtype=author&query=Bagci%2C+U">Ulas Bagci</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.04491v1-abstract-short" style="display: inline;"> Liver cirrhosis, a leading cause of global mortality, requires precise segmentation of ROIs for effective disease monitoring and treatment planning. Existing segmentation models often fail to capture complex feature interactions and generalize across diverse datasets. To address these limitations, we propose a novel synergistic theory that leverages complementary latent spaces for enhanced feature… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.04491v1-abstract-full').style.display = 'inline'; document.getElementById('2408.04491v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.04491v1-abstract-full" style="display: none;"> Liver cirrhosis, a leading cause of global mortality, requires precise segmentation of ROIs for effective disease monitoring and treatment planning. Existing segmentation models often fail to capture complex feature interactions and generalize across diverse datasets. To address these limitations, we propose a novel synergistic theory that leverages complementary latent spaces for enhanced feature interaction modeling. Our proposed architecture, nnSynergyNet3D integrates continuous and discrete latent spaces for 3D volumes and features auto-configured training. This approach captures both fine-grained and coarse features, enabling effective modeling of intricate feature interactions. We empirically validated nnSynergyNet3D on a private dataset of 628 high-resolution T1 abdominal MRI scans from 339 patients. Our model outperformed the baseline nnUNet3D by approximately 2%. Additionally, zero-shot testing on healthy liver CT scans from the public LiTS dataset demonstrated superior cross-modal generalization capabilities. These results highlight the potential of synergistic latent space models to improve segmentation accuracy and robustness, thereby enhancing clinical workflows by ensuring consistency across CT and MRI modalities. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.04491v1-abstract-full').style.display = 'none'; document.getElementById('2408.04491v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.12367">arXiv:2405.12367</a> <span> [<a href="https://arxiv.org/pdf/2405.12367">pdf</a>, <a href="https://arxiv.org/format/2405.12367">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Large-Scale Multi-Center CT and MRI Segmentation of Pancreas with Deep Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhang%2C+Z">Zheyuan Zhang</a>, <a href="/search/?searchtype=author&query=Keles%2C+E">Elif Keles</a>, <a href="/search/?searchtype=author&query=Durak%2C+G">Gorkem Durak</a>, <a href="/search/?searchtype=author&query=Taktak%2C+Y">Yavuz Taktak</a>, <a href="/search/?searchtype=author&query=Susladkar%2C+O">Onkar Susladkar</a>, <a href="/search/?searchtype=author&query=Gorade%2C+V">Vandan Gorade</a>, <a href="/search/?searchtype=author&query=Jha%2C+D">Debesh Jha</a>, <a href="/search/?searchtype=author&query=Ormeci%2C+A+C">Asli C. Ormeci</a>, <a href="/search/?searchtype=author&query=Medetalibeyoglu%2C+A">Alpay Medetalibeyoglu</a>, <a href="/search/?searchtype=author&query=Yao%2C+L">Lanhong Yao</a>, <a href="/search/?searchtype=author&query=Wang%2C+B">Bin Wang</a>, <a href="/search/?searchtype=author&query=Isler%2C+I+S">Ilkin Sevgi Isler</a>, <a href="/search/?searchtype=author&query=Peng%2C+L">Linkai Peng</a>, <a href="/search/?searchtype=author&query=Pan%2C+H">Hongyi Pan</a>, <a href="/search/?searchtype=author&query=Vendrami%2C+C+L">Camila Lopes Vendrami</a>, <a href="/search/?searchtype=author&query=Bourhani%2C+A">Amir Bourhani</a>, <a href="/search/?searchtype=author&query=Velichko%2C+Y">Yury Velichko</a>, <a href="/search/?searchtype=author&query=Gong%2C+B">Boqing Gong</a>, <a href="/search/?searchtype=author&query=Spampinato%2C+C">Concetto Spampinato</a>, <a href="/search/?searchtype=author&query=Pyrros%2C+A">Ayis Pyrros</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Pallavi Tiwari</a>, <a href="/search/?searchtype=author&query=Klatte%2C+D+C+F">Derk C. F. Klatte</a>, <a href="/search/?searchtype=author&query=Engels%2C+M">Megan Engels</a>, <a href="/search/?searchtype=author&query=Hoogenboom%2C+S">Sanne Hoogenboom</a>, <a href="/search/?searchtype=author&query=Bolan%2C+C+W">Candice W. Bolan</a> , et al. (13 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.12367v3-abstract-short" style="display: inline;"> Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic segmentation is more established, MRI-based segmentation methods are understudied, largely due to a lack of publicly available datasets, benchmarking research efforts, and domain-specific deep learning methods. In this retrospective st… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.12367v3-abstract-full').style.display = 'inline'; document.getElementById('2405.12367v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.12367v3-abstract-full" style="display: none;"> Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic segmentation is more established, MRI-based segmentation methods are understudied, largely due to a lack of publicly available datasets, benchmarking research efforts, and domain-specific deep learning methods. In this retrospective study, we collected a large dataset (767 scans from 499 participants) of T1-weighted (T1W) and T2-weighted (T2W) abdominal MRI series from five centers between March 2004 and November 2022. We also collected CT scans of 1,350 patients from publicly available sources for benchmarking purposes. We developed a new pancreas segmentation method, called PanSegNet, combining the strengths of nnUNet and a Transformer network with a new linear attention module enabling volumetric computation. We tested PanSegNet's accuracy in cross-modality (a total of 2,117 scans) and cross-center settings with Dice and Hausdorff distance (HD95) evaluation metrics. We used Cohen's kappa statistics for intra and inter-rater agreement evaluation and paired t-tests for volume and Dice comparisons, respectively. For segmentation accuracy, we achieved Dice coefficients of 88.3% (std: 7.2%, at case level) with CT, 85.0% (std: 7.9%) with T1W MRI, and 86.3% (std: 6.4%) with T2W MRI. There was a high correlation for pancreas volume prediction with R^2 of 0.91, 0.84, and 0.85 for CT, T1W, and T2W, respectively. We found moderate inter-observer (0.624 and 0.638 for T1W and T2W MRI, respectively) and high intra-observer agreement scores. All MRI data is made available at https://osf.io/kysnj/. Our source code is available at https://github.com/NUBagciLab/PaNSegNet. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.12367v3-abstract-full').style.display = 'none'; document.getElementById('2405.12367v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Peer-reviewer version</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.13586">arXiv:2404.13586</a> <span> [<a href="https://arxiv.org/pdf/2404.13586">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Earth and Planetary Astrophysics">astro-ph.EP</span> </div> </div> <p class="title is-5 mathjax"> The PEPSI Exoplanet Transit Survey (PETS). V: New Na D transmission spectra indicate a quieter atmosphere on HD 189733b </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Keles%2C+E">E. Keles</a>, <a href="/search/?searchtype=author&query=Czesla%2C+S">S. Czesla</a>, <a href="/search/?searchtype=author&query=Poppenhaeger%2C+K">K. Poppenhaeger</a>, <a href="/search/?searchtype=author&query=Hauschildt%2C+P">P. Hauschildt</a>, <a href="/search/?searchtype=author&query=Carroll%2C+T+A">T. A. Carroll</a>, <a href="/search/?searchtype=author&query=Ilyin%2C+I">I. Ilyin</a>, <a href="/search/?searchtype=author&query=Baratella%2C+M">M. Baratella</a>, <a href="/search/?searchtype=author&query=Steffen%2C+M">M. Steffen</a>, <a href="/search/?searchtype=author&query=Strassmeier%2C+K+G">K. G. Strassmeier</a>, <a href="/search/?searchtype=author&query=Bonomo%2C+A+S">A. S. Bonomo</a>, <a href="/search/?searchtype=author&query=Gaudi%2C+B+S">B. S. Gaudi</a>, <a href="/search/?searchtype=author&query=Henning%2C+T">T. Henning</a>, <a href="/search/?searchtype=author&query=Johnson%2C+M+C">M. C. Johnson</a>, <a href="/search/?searchtype=author&query=Molaverdikhani%2C+K">K. Molaverdikhani</a>, <a href="/search/?searchtype=author&query=Nascimbeni%2C+V">V. Nascimbeni</a>, <a href="/search/?searchtype=author&query=Patience%2C+J">J. Patience</a>, <a href="/search/?searchtype=author&query=Reiners%2C+A">A. Reiners</a>, <a href="/search/?searchtype=author&query=Scandariato%2C+G">G. Scandariato</a>, <a href="/search/?searchtype=author&query=Schlawin%2C+E">E. Schlawin</a>, <a href="/search/?searchtype=author&query=Shkolnik%2C+E">E. Shkolnik</a>, <a href="/search/?searchtype=author&query=Sicilia%2C+D">D. Sicilia</a>, <a href="/search/?searchtype=author&query=Sozzetti%2C+A">A. Sozzetti</a>, <a href="/search/?searchtype=author&query=Mallonn%2C+M">M. Mallonn</a>, <a href="/search/?searchtype=author&query=Veillet%2C+C">C. Veillet</a>, <a href="/search/?searchtype=author&query=Wang%2C+J">J. Wang</a> , et al. (1 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.13586v1-abstract-short" style="display: inline;"> Absorption lines from exoplanet atmospheres observed in transmission allow us to study atmospheric characteristics such as winds. We present a new high-resolution transit time-series of HD 189733b, acquired with the PEPSI instrument at the LBT and analyze the transmission spectrum around the Na D lines. We model the spectral signature of the RM-CLV-effect using synthetic PHOENIX spectra based on s… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.13586v1-abstract-full').style.display = 'inline'; document.getElementById('2404.13586v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.13586v1-abstract-full" style="display: none;"> Absorption lines from exoplanet atmospheres observed in transmission allow us to study atmospheric characteristics such as winds. We present a new high-resolution transit time-series of HD 189733b, acquired with the PEPSI instrument at the LBT and analyze the transmission spectrum around the Na D lines. We model the spectral signature of the RM-CLV-effect using synthetic PHOENIX spectra based on spherical LTE atmospheric models. We find a Na D absorption signature between the second and third contact but not during the ingress and egress phases, which casts doubt on the planetary origin of the signal. Presupposing a planetary origin of the signal, the results suggest a weak day-to-nightside streaming wind in the order of 0.7 km/s and a moderate super-rotational streaming wind in the order of 3 - 4 km/s, challenging claims of prevailing strong winds on HD 189733b. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.13586v1-abstract-full').style.display = 'none'; document.getElementById('2404.13586v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted for publication in Monthly Notices of the Royal Astronomical Society</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.06961">arXiv:2403.06961</a> <span> [<a href="https://arxiv.org/pdf/2403.06961">pdf</a>, <a href="https://arxiv.org/format/2403.06961">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Explainable Transformer Prototypes for Medical Diagnoses </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Demir%2C+U">Ugur Demir</a>, <a href="/search/?searchtype=author&query=Jha%2C+D">Debesh Jha</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Z">Zheyuan Zhang</a>, <a href="/search/?searchtype=author&query=Keles%2C+E">Elif Keles</a>, <a href="/search/?searchtype=author&query=Allen%2C+B">Bradley Allen</a>, <a href="/search/?searchtype=author&query=Katsaggelos%2C+A+K">Aggelos K. Katsaggelos</a>, <a href="/search/?searchtype=author&query=Bagci%2C+U">Ulas Bagci</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.06961v1-abstract-short" style="display: inline;"> Deployments of artificial intelligence in medical diagnostics mandate not just accuracy and efficacy but also trust, emphasizing the need for explainability in machine decisions. The recent trend in automated medical image diagnostics leans towards the deployment of Transformer-based architectures, credited to their impressive capabilities. Since the self-attention feature of transformers contribu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.06961v1-abstract-full').style.display = 'inline'; document.getElementById('2403.06961v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.06961v1-abstract-full" style="display: none;"> Deployments of artificial intelligence in medical diagnostics mandate not just accuracy and efficacy but also trust, emphasizing the need for explainability in machine decisions. The recent trend in automated medical image diagnostics leans towards the deployment of Transformer-based architectures, credited to their impressive capabilities. Since the self-attention feature of transformers contributes towards identifying crucial regions during the classification process, they enhance the trustability of the methods. However, the complex intricacies of these attention mechanisms may fall short of effectively pinpointing the regions of interest directly influencing AI decisions. Our research endeavors to innovate a unique attention block that underscores the correlation between 'regions' rather than 'pixels'. To address this challenge, we introduce an innovative system grounded in prototype learning, featuring an advanced self-attention mechanism that goes beyond conventional ad-hoc visual explanation techniques by offering comprehensible visual insights. A combined quantitative and qualitative methodological approach was used to demonstrate the effectiveness of the proposed method on the large-scale NIH chest X-ray dataset. Experimental results showed that our proposed method offers a promising direction for explainability, which can lead to the development of more trustable systems, which can facilitate easier and rapid adoption of such technology into routine clinics. The code is available at www.github.com/NUBagcilab/r2r_proto. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.06961v1-abstract-full').style.display = 'none'; document.getElementById('2403.06961v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.18054">arXiv:2311.18054</a> <span> [<a href="https://arxiv.org/pdf/2311.18054">pdf</a>, <a href="https://arxiv.org/format/2311.18054">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> I Know You Did Not Write That! A Sampling Based Watermarking Method for Identifying Machine Generated Text </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Kele%C5%9F%2C+K+E">Kaan Efe Kele艧</a>, <a href="/search/?searchtype=author&query=G%C3%BCrb%C3%BCz%2C+%C3%96+K">脰mer Kaan G眉rb眉z</a>, <a href="/search/?searchtype=author&query=Kutlu%2C+M">Mucahid Kutlu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.18054v2-abstract-short" style="display: inline;"> Potential harms of Large Language Models such as mass misinformation and plagiarism can be partially mitigated if there exists a reliable way to detect machine generated text. In this paper, we propose a new watermarking method to detect machine-generated texts. Our method embeds a unique pattern within the generated text, ensuring that while the content remains coherent and natural to human reade… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.18054v2-abstract-full').style.display = 'inline'; document.getElementById('2311.18054v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.18054v2-abstract-full" style="display: none;"> Potential harms of Large Language Models such as mass misinformation and plagiarism can be partially mitigated if there exists a reliable way to detect machine generated text. In this paper, we propose a new watermarking method to detect machine-generated texts. Our method embeds a unique pattern within the generated text, ensuring that while the content remains coherent and natural to human readers, it carries distinct markers that can be identified algorithmically. Specifically, we intervene with the token sampling process in a way which enables us to trace back our token choices during the detection phase. We show how watermarking affects textual quality and compare our proposed method with a state-of-the-art watermarking method in terms of robustness and detectability. Through extensive experiments, we demonstrate the effectiveness of our watermarking scheme in distinguishing between watermarked and non-watermarked text, achieving high detection rates while maintaining textual quality. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.18054v2-abstract-full').style.display = 'none'; document.getElementById('2311.18054v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 29 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.12868">arXiv:2310.12868</a> <span> [<a href="https://arxiv.org/pdf/2310.12868">pdf</a>, <a href="https://arxiv.org/format/2310.12868">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> EMIT-Diff: Enhancing Medical Image Segmentation via Text-Guided Diffusion Model </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhang%2C+Z">Zheyuan Zhang</a>, <a href="/search/?searchtype=author&query=Yao%2C+L">Lanhong Yao</a>, <a href="/search/?searchtype=author&query=Wang%2C+B">Bin Wang</a>, <a href="/search/?searchtype=author&query=Jha%2C+D">Debesh Jha</a>, <a href="/search/?searchtype=author&query=Keles%2C+E">Elif Keles</a>, <a href="/search/?searchtype=author&query=Medetalibeyoglu%2C+A">Alpay Medetalibeyoglu</a>, <a href="/search/?searchtype=author&query=Bagci%2C+U">Ulas Bagci</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.12868v1-abstract-short" style="display: inline;"> Large-scale, big-variant, and high-quality data are crucial for developing robust and successful deep-learning models for medical applications since they potentially enable better generalization performance and avoid overfitting. However, the scarcity of high-quality labeled data always presents significant challenges. This paper proposes a novel approach to address this challenge by developing co… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.12868v1-abstract-full').style.display = 'inline'; document.getElementById('2310.12868v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.12868v1-abstract-full" style="display: none;"> Large-scale, big-variant, and high-quality data are crucial for developing robust and successful deep-learning models for medical applications since they potentially enable better generalization performance and avoid overfitting. However, the scarcity of high-quality labeled data always presents significant challenges. This paper proposes a novel approach to address this challenge by developing controllable diffusion models for medical image synthesis, called EMIT-Diff. We leverage recent diffusion probabilistic models to generate realistic and diverse synthetic medical image data that preserve the essential characteristics of the original medical images by incorporating edge information of objects to guide the synthesis process. In our approach, we ensure that the synthesized samples adhere to medically relevant constraints and preserve the underlying structure of imaging data. Due to the random sampling process by the diffusion model, we can generate an arbitrary number of synthetic images with diverse appearances. To validate the effectiveness of our proposed method, we conduct an extensive set of medical image segmentation experiments on multiple datasets, including Ultrasound breast (+13.87%), CT spleen (+0.38%), and MRI prostate (+7.78%), achieving significant improvements over the baseline segmentation methods. For the first time, to our best knowledge, the promising results demonstrate the effectiveness of our EMIT-Diff for medical image segmentation tasks and show the feasibility of introducing a first-ever text-guided diffusion model for general medical image segmentation tasks. With carefully designed ablation experiments, we investigate the influence of various data augmentation ratios, hyper-parameter settings, patch size for generating random merging mask settings, and combined influence with different network architectures. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.12868v1-abstract-full').style.display = 'none'; document.getElementById('2310.12868v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">15 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.09352">arXiv:2310.09352</a> <span> [<a href="https://arxiv.org/pdf/2310.09352">pdf</a>, <a href="https://arxiv.org/format/2310.09352">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Earth and Planetary Astrophysics">astro-ph.EP</span> </div> </div> <p class="title is-5 mathjax"> The PEPSI Exoplanet Transit Survey (PETS) IV: Assessing the atmospheric chemistry of KELT-20b </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Petz%2C+S">Sydney Petz</a>, <a href="/search/?searchtype=author&query=Johnson%2C+M+C">Marshall C. Johnson</a>, <a href="/search/?searchtype=author&query=Asnodkar%2C+A+P">Anusha Pai Asnodkar</a>, <a href="/search/?searchtype=author&query=Wang%2C+J">Ji Wang</a>, <a href="/search/?searchtype=author&query=Gaudi%2C+B+S">B. Scott Gaudi</a>, <a href="/search/?searchtype=author&query=Henning%2C+T">Thomas Henning</a>, <a href="/search/?searchtype=author&query=Keles%2C+E">Engin Keles</a>, <a href="/search/?searchtype=author&query=Molaverdikhani%2C+K">Karan Molaverdikhani</a>, <a href="/search/?searchtype=author&query=Poppenhaeger%2C+K">Katja Poppenhaeger</a>, <a href="/search/?searchtype=author&query=Scandariato%2C+G">Gaetano Scandariato</a>, <a href="/search/?searchtype=author&query=Shkolnik%2C+E+K">Evgenya K. Shkolnik</a>, <a href="/search/?searchtype=author&query=Sicilia%2C+D">Daniela Sicilia</a>, <a href="/search/?searchtype=author&query=Strassmeier%2C+K+G">Klaus G. Strassmeier</a>, <a href="/search/?searchtype=author&query=Yan%2C+F">Fei Yan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.09352v1-abstract-short" style="display: inline;"> Most ultra hot Jupiters (UHJs) show evidence of temperature inversions, in which temperature increases with altitude over a range of pressures. Temperature inversions can occur when there is a species that absorbs the stellar irradiation at a relatively high level of the atmospheres. However, the species responsible for this absorption remains unidentified. In particular, the UHJ KELT-20b is known… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.09352v1-abstract-full').style.display = 'inline'; document.getElementById('2310.09352v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.09352v1-abstract-full" style="display: none;"> Most ultra hot Jupiters (UHJs) show evidence of temperature inversions, in which temperature increases with altitude over a range of pressures. Temperature inversions can occur when there is a species that absorbs the stellar irradiation at a relatively high level of the atmospheres. However, the species responsible for this absorption remains unidentified. In particular, the UHJ KELT-20b is known to have a temperature inversion. Using high resolution emission spectroscopy from LBT/PEPSI we investigate the atomic and molecular opacity sources that may cause the inversion in KELT-20b, as well as explore its atmospheric chemistry. We confirm the presence of Fe I with a significance of 17$蟽$. We also report a tentative $4.3蟽$ detection of Ni I. A nominally $4.5蟽$ detection of Mg I emission in the PEPSI blue arm is likely in fact due to aliasing between the Mg I cross-correlation template and the Fe I lines present in the spectrum. We cannot reproduce a recent detection of Cr I, while we do not have the wavelength coverage to robustly test past detections of Fe II and Si I. Together with non-detections of molecular species like TiO, this suggests that Fe I is likely to be the dominant optical opacity source in the dayside atmosphere of KELT-20b and may be responsible for the temperature inversion. We explore ways to reconcile the differences between our results and those in literature and point to future paths to understand atmospheric variability. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.09352v1-abstract-full').style.display = 'none'; document.getElementById('2310.09352v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Revised version resubmitted to MNRAS. 15 pages, 8 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.05857">arXiv:2309.05857</a> <span> [<a href="https://arxiv.org/pdf/2309.05857">pdf</a>, <a href="https://arxiv.org/format/2309.05857">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Radiomics Boosts Deep Learning Model for IPMN Classification </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Yao%2C+L">Lanhong Yao</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Z">Zheyuan Zhang</a>, <a href="/search/?searchtype=author&query=Demir%2C+U">Ugur Demir</a>, <a href="/search/?searchtype=author&query=Keles%2C+E">Elif Keles</a>, <a href="/search/?searchtype=author&query=Vendrami%2C+C">Camila Vendrami</a>, <a href="/search/?searchtype=author&query=Agarunov%2C+E">Emil Agarunov</a>, <a href="/search/?searchtype=author&query=Bolan%2C+C">Candice Bolan</a>, <a href="/search/?searchtype=author&query=Schoots%2C+I">Ivo Schoots</a>, <a href="/search/?searchtype=author&query=Bruno%2C+M">Marc Bruno</a>, <a href="/search/?searchtype=author&query=Keswani%2C+R">Rajesh Keswani</a>, <a href="/search/?searchtype=author&query=Miller%2C+F">Frank Miller</a>, <a href="/search/?searchtype=author&query=Gonda%2C+T">Tamas Gonda</a>, <a href="/search/?searchtype=author&query=Yazici%2C+C">Cemal Yazici</a>, <a href="/search/?searchtype=author&query=Tirkes%2C+T">Temel Tirkes</a>, <a href="/search/?searchtype=author&query=Wallace%2C+M">Michael Wallace</a>, <a href="/search/?searchtype=author&query=Spampinato%2C+C">Concetto Spampinato</a>, <a href="/search/?searchtype=author&query=Bagci%2C+U">Ulas Bagci</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2309.05857v1-abstract-short" style="display: inline;"> Intraductal Papillary Mucinous Neoplasm (IPMN) cysts are pre-malignant pancreas lesions, and they can progress into pancreatic cancer. Therefore, detecting and stratifying their risk level is of ultimate importance for effective treatment planning and disease control. However, this is a highly challenging task because of the diverse and irregular shape, texture, and size of the IPMN cysts as well… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.05857v1-abstract-full').style.display = 'inline'; document.getElementById('2309.05857v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.05857v1-abstract-full" style="display: none;"> Intraductal Papillary Mucinous Neoplasm (IPMN) cysts are pre-malignant pancreas lesions, and they can progress into pancreatic cancer. Therefore, detecting and stratifying their risk level is of ultimate importance for effective treatment planning and disease control. However, this is a highly challenging task because of the diverse and irregular shape, texture, and size of the IPMN cysts as well as the pancreas. In this study, we propose a novel computer-aided diagnosis pipeline for IPMN risk classification from multi-contrast MRI scans. Our proposed analysis framework includes an efficient volumetric self-adapting segmentation strategy for pancreas delineation, followed by a newly designed deep learning-based classification scheme with a radiomics-based predictive approach. We test our proposed decision-fusion model in multi-center data sets of 246 multi-contrast MRI scans and obtain superior performance to the state of the art (SOTA) in this field. Our ablation studies demonstrate the significance of both radiomics and deep learning modules for achieving the new SOTA performance compared to international guidelines and published studies (81.9\% vs 61.3\% in accuracy). Our findings have important implications for clinical decision-making. In a series of rigorous experiments on multi-center data sets (246 MRI scans from five centers), we achieved unprecedented performance (81.9\% accuracy). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.05857v1-abstract-full').style.display = 'none'; document.getElementById('2309.05857v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages, MICCAI MLMI 2023</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.18221">arXiv:2305.18221</a> <span> [<a href="https://arxiv.org/pdf/2305.18221">pdf</a>, <a href="https://arxiv.org/format/2305.18221">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> GazeGNN: A Gaze-Guided Graph Neural Network for Chest X-ray Classification </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Wang%2C+B">Bin Wang</a>, <a href="/search/?searchtype=author&query=Pan%2C+H">Hongyi Pan</a>, <a href="/search/?searchtype=author&query=Aboah%2C+A">Armstrong Aboah</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Z">Zheyuan Zhang</a>, <a href="/search/?searchtype=author&query=Keles%2C+E">Elif Keles</a>, <a href="/search/?searchtype=author&query=Torigian%2C+D">Drew Torigian</a>, <a href="/search/?searchtype=author&query=Turkbey%2C+B">Baris Turkbey</a>, <a href="/search/?searchtype=author&query=Krupinski%2C+E">Elizabeth Krupinski</a>, <a href="/search/?searchtype=author&query=Udupa%2C+J">Jayaram Udupa</a>, <a href="/search/?searchtype=author&query=Bagci%2C+U">Ulas Bagci</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2305.18221v3-abstract-short" style="display: inline;"> Eye tracking research is important in computer vision because it can help us understand how humans interact with the visual world. Specifically for high-risk applications, such as in medical imaging, eye tracking can help us to comprehend how radiologists and other medical professionals search, analyze, and interpret images for diagnostic and clinical purposes. Hence, the application of eye tracki… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.18221v3-abstract-full').style.display = 'inline'; document.getElementById('2305.18221v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.18221v3-abstract-full" style="display: none;"> Eye tracking research is important in computer vision because it can help us understand how humans interact with the visual world. Specifically for high-risk applications, such as in medical imaging, eye tracking can help us to comprehend how radiologists and other medical professionals search, analyze, and interpret images for diagnostic and clinical purposes. Hence, the application of eye tracking techniques in disease classification has become increasingly popular in recent years. Contemporary works usually transform gaze information collected by eye tracking devices into visual attention maps (VAMs) to supervise the learning process. However, this is a time-consuming preprocessing step, which stops us from applying eye tracking to radiologists' daily work. To solve this problem, we propose a novel gaze-guided graph neural network (GNN), GazeGNN, to leverage raw eye-gaze data without being converted into VAMs. In GazeGNN, to directly integrate eye gaze into image classification, we create a unified representation graph that models both images and gaze pattern information. With this benefit, we develop a real-time, real-world, end-to-end disease classification algorithm for the first time in the literature. This achievement demonstrates the practicality and feasibility of integrating real-time eye tracking techniques into the daily work of radiologists. To our best knowledge, GazeGNN is the first work that adopts GNN to integrate image and eye-gaze data. Our experiments on the public chest X-ray dataset show that our proposed method exhibits the best classification performance compared to existing methods. The code is available at https://github.com/ukaukaaaa/GazeGNN. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.18221v3-abstract-full').style.display = 'none'; document.getElementById('2305.18221v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 29 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">WACV 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2304.11530">arXiv:2304.11530</a> <span> [<a href="https://arxiv.org/pdf/2304.11530">pdf</a>, <a href="https://arxiv.org/format/2304.11530">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Ensuring Trustworthy Medical Artificial Intelligence through Ethical and Philosophical Principles </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Jha%2C+D">Debesh Jha</a>, <a href="/search/?searchtype=author&query=Rauniyar%2C+A">Ashish Rauniyar</a>, <a href="/search/?searchtype=author&query=Srivastava%2C+A">Abhiskek Srivastava</a>, <a href="/search/?searchtype=author&query=Hagos%2C+D+H">Desta Haileselassie Hagos</a>, <a href="/search/?searchtype=author&query=Tomar%2C+N+K">Nikhil Kumar Tomar</a>, <a href="/search/?searchtype=author&query=Sharma%2C+V">Vanshali Sharma</a>, <a href="/search/?searchtype=author&query=Keles%2C+E">Elif Keles</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Z">Zheyuan Zhang</a>, <a href="/search/?searchtype=author&query=Demir%2C+U">Ugur Demir</a>, <a href="/search/?searchtype=author&query=Topcu%2C+A">Ahmet Topcu</a>, <a href="/search/?searchtype=author&query=Yazidi%2C+A">Anis Yazidi</a>, <a href="/search/?searchtype=author&query=H%C3%A5akeg%C3%A5rd%2C+J+E">Jan Erik H氓akeg氓rd</a>, <a href="/search/?searchtype=author&query=Bagci%2C+U">Ulas Bagci</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2304.11530v4-abstract-short" style="display: inline;"> Artificial intelligence (AI) methods hold immense potential to revolutionize numerous medical care by enhancing the experience of medical experts and patients. AI-based computer-assisted diagnosis and treatment tools can democratize healthcare by matching the clinical level or surpassing clinical experts. As a result, advanced healthcare services can be affordable to all populations, irrespective… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.11530v4-abstract-full').style.display = 'inline'; document.getElementById('2304.11530v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2304.11530v4-abstract-full" style="display: none;"> Artificial intelligence (AI) methods hold immense potential to revolutionize numerous medical care by enhancing the experience of medical experts and patients. AI-based computer-assisted diagnosis and treatment tools can democratize healthcare by matching the clinical level or surpassing clinical experts. As a result, advanced healthcare services can be affordable to all populations, irrespective of demographics, race, or socioeconomic background. The democratization of such AI tools can reduce the cost of care, optimize resource allocation, and improve the quality of care. In contrast to humans, AI can uncover complex relations in the data from a large set of inputs and even lead to new evidence-based knowledge in medicine. However, integrating AI into healthcare raises several ethical and philosophical concerns, such as bias, transparency, autonomy, responsibility, and accountability. Here, we emphasize recent advances in AI-assisted medical image analysis, existing standards, and the significance of comprehending ethical issues and best practices for clinical settings. We cover the technical and ethical challenges and implications of deploying AI in hospitals and public organizations. We also discuss key measures and techniques to address ethical challenges, data scarcity, racial bias, lack of transparency, and algorithmic bias and provide recommendations and future directions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.11530v4-abstract-full').style.display = 'none'; document.getElementById('2304.11530v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 23 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2302.02289">arXiv:2302.02289</a> <span> [<a href="https://arxiv.org/pdf/2302.02289">pdf</a>, <a href="https://arxiv.org/format/2302.02289">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Selecting the Best Optimizers for Deep Learning based Medical Image Segmentation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Mortazi%2C+A">Aliasghar Mortazi</a>, <a href="/search/?searchtype=author&query=Cicek%2C+V">Vedat Cicek</a>, <a href="/search/?searchtype=author&query=Keles%2C+E">Elif Keles</a>, <a href="/search/?searchtype=author&query=Bagci%2C+U">Ulas Bagci</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2302.02289v1-abstract-short" style="display: inline;"> The goal of this work is to identify the best optimizers for deep learning in the context of cardiac image segmentation and to provide guidance on how to design segmentation networks with effective optimization strategies. Adaptive learning helps with fast convergence by starting with a larger learning rate (LR) and gradually decreasing it. Momentum optimizers are particularly effective at quickly… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.02289v1-abstract-full').style.display = 'inline'; document.getElementById('2302.02289v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2302.02289v1-abstract-full" style="display: none;"> The goal of this work is to identify the best optimizers for deep learning in the context of cardiac image segmentation and to provide guidance on how to design segmentation networks with effective optimization strategies. Adaptive learning helps with fast convergence by starting with a larger learning rate (LR) and gradually decreasing it. Momentum optimizers are particularly effective at quickly optimizing neural networks within the accelerated schemes category. By revealing the potential interplay between these two types of algorithms (LR and momentum optimizers or momentum rate (MR) in short), in this article, we explore the two variants of SGD algorithms in a single setting. We suggest using cyclic learning as the base optimizer and integrating optimal values of learning rate and momentum rate. We investigated the relationship of LR and MR under an important problem of medical image segmentation of cardiac structures from MRI and CT scans. We conducted experiments using the cardiac imaging dataset from the ACDC challenge of MICCAI 2017, and four different architectures shown to be successful for cardiac image segmentation problems. Our comprehensive evaluations demonstrated that the proposed optimizer achieved better results (over a 2\% improvement in the dice metric) than other optimizers in deep learning literature with similar or lower computational cost in both single and multi-object segmentation settings. We hypothesized that combination of accelerated and adaptive optimization methods can have a drastic effect in medical image segmentation performances. To this end, we proposed a new cyclic optimization method (\textit{CLMR}) to address the efficiency and accuracy problems in deep learning based medical image segmentation. The proposed strategy yielded better generalization in comparison to adaptive optimizers. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.02289v1-abstract-full').style.display = 'none'; document.getElementById('2302.02289v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 February, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2302.00225">arXiv:2302.00225</a> <span> [<a href="https://arxiv.org/pdf/2302.00225">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> The Past, Current, and Future of Neonatal Intensive Care Units with Artificial Intelligence </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Keles%2C+E">Elif Keles</a>, <a href="/search/?searchtype=author&query=Bagci%2C+U">Ulas Bagci</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2302.00225v2-abstract-short" style="display: inline;"> Machine learning and deep learning are two subsets of artificial intelligence that involve teaching computers to learn and make decisions from any sort of data. Most recent developments in artificial intelligence are coming from deep learning, which has proven revolutionary in almost all fields, from computer vision to health sciences. The effects of deep learning in medicine have changed the conv… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.00225v2-abstract-full').style.display = 'inline'; document.getElementById('2302.00225v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2302.00225v2-abstract-full" style="display: none;"> Machine learning and deep learning are two subsets of artificial intelligence that involve teaching computers to learn and make decisions from any sort of data. Most recent developments in artificial intelligence are coming from deep learning, which has proven revolutionary in almost all fields, from computer vision to health sciences. The effects of deep learning in medicine have changed the conventional ways of clinical application significantly. Although some sub-fields of medicine, such as pediatrics, have been relatively slow in receiving the critical benefits of deep learning, related research in pediatrics has started to accumulate to a significant level, too. Hence, in this paper, we review recently developed machine learning and deep learning-based solutions for neonatology applications. We systematically evaluate the roles of both classical machine learning and deep learning in neonatology applications, define the methodologies, including algorithmic developments, and describe the remaining challenges in the assessment of neonatal diseases by using PRISMA 2020 guidelines. To date, the primary areas of focus in neonatology regarding AI applications have included survival analysis, neuroimaging, analysis of vital parameters and biosignals, and retinopathy of prematurity diagnosis. We have categorically summarized 106 research articles from 1996 to 2022 and discussed their pros and cons, respectively. In this systematic review, we aimed to further enhance the comprehensiveness of the study. We also discuss possible directions for new AI models and the future of neonatology with the rising power of AI, suggesting roadmaps for the integration of AI into neonatal intensive care units. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.00225v2-abstract-full').style.display = 'none'; document.getElementById('2302.00225v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 31 January, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">90 pages, review article</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2301.02181">arXiv:2301.02181</a> <span> [<a href="https://arxiv.org/pdf/2301.02181">pdf</a>, <a href="https://arxiv.org/format/2301.02181">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> A Critical Appraisal of Data Augmentation Methods for Imaging-Based Medical Diagnosis Applications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Pattilachan%2C+T+M">Tara M. Pattilachan</a>, <a href="/search/?searchtype=author&query=Demir%2C+U">Ugur Demir</a>, <a href="/search/?searchtype=author&query=Keles%2C+E">Elif Keles</a>, <a href="/search/?searchtype=author&query=Jha%2C+D">Debesh Jha</a>, <a href="/search/?searchtype=author&query=Klatte%2C+D">Derk Klatte</a>, <a href="/search/?searchtype=author&query=Engels%2C+M">Megan Engels</a>, <a href="/search/?searchtype=author&query=Hoogenboom%2C+S">Sanne Hoogenboom</a>, <a href="/search/?searchtype=author&query=Bolan%2C+C">Candice Bolan</a>, <a href="/search/?searchtype=author&query=Wallace%2C+M">Michael Wallace</a>, <a href="/search/?searchtype=author&query=Bagci%2C+U">Ulas Bagci</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2301.02181v1-abstract-short" style="display: inline;"> Current data augmentation techniques and transformations are well suited for improving the size and quality of natural image datasets but are not yet optimized for medical imaging. We hypothesize that sub-optimal data augmentations can easily distort or occlude medical images, leading to false positives or negatives during patient diagnosis, prediction, or therapy/surgery evaluation. In our experi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.02181v1-abstract-full').style.display = 'inline'; document.getElementById('2301.02181v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2301.02181v1-abstract-full" style="display: none;"> Current data augmentation techniques and transformations are well suited for improving the size and quality of natural image datasets but are not yet optimized for medical imaging. We hypothesize that sub-optimal data augmentations can easily distort or occlude medical images, leading to false positives or negatives during patient diagnosis, prediction, or therapy/surgery evaluation. In our experimental results, we found that utilizing commonly used intensity-based data augmentation distorts the MRI scans and leads to texture information loss, thus negatively affecting the overall performance of classification. Additionally, we observed that commonly used data augmentation methods cannot be used with a plug-and-play approach in medical imaging, and requires manual tuning and adjustment. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.02181v1-abstract-full').style.display = 'none'; document.getElementById('2301.02181v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 December, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2208.07417">arXiv:2208.07417</a> <span> [<a href="https://arxiv.org/pdf/2208.07417">pdf</a>, <a href="https://arxiv.org/format/2208.07417">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> An Efficient Multi-Scale Fusion Network for 3D Organ at Risk (OAR) Segmentation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Srivastava%2C+A">Abhishek Srivastava</a>, <a href="/search/?searchtype=author&query=Jha%2C+D">Debesh Jha</a>, <a href="/search/?searchtype=author&query=Keles%2C+E">Elif Keles</a>, <a href="/search/?searchtype=author&query=Aydogan%2C+B">Bulent Aydogan</a>, <a href="/search/?searchtype=author&query=Abazeed%2C+M">Mohamed Abazeed</a>, <a href="/search/?searchtype=author&query=Bagci%2C+U">Ulas Bagci</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2208.07417v1-abstract-short" style="display: inline;"> Accurate segmentation of organs-at-risks (OARs) is a precursor for optimizing radiation therapy planning. Existing deep learning-based multi-scale fusion architectures have demonstrated a tremendous capacity for 2D medical image segmentation. The key to their success is aggregating global context and maintaining high resolution representations. However, when translated into 3D segmentation problem… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.07417v1-abstract-full').style.display = 'inline'; document.getElementById('2208.07417v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2208.07417v1-abstract-full" style="display: none;"> Accurate segmentation of organs-at-risks (OARs) is a precursor for optimizing radiation therapy planning. Existing deep learning-based multi-scale fusion architectures have demonstrated a tremendous capacity for 2D medical image segmentation. The key to their success is aggregating global context and maintaining high resolution representations. However, when translated into 3D segmentation problems, existing multi-scale fusion architectures might underperform due to their heavy computation overhead and substantial data diet. To address this issue, we propose a new OAR segmentation framework, called OARFocalFuseNet, which fuses multi-scale features and employs focal modulation for capturing global-local context across multiple scales. Each resolution stream is enriched with features from different resolution scales, and multi-scale information is aggregated to model diverse contextual ranges. As a result, feature representations are further boosted. The comprehensive comparisons in our experimental setup with OAR segmentation as well as multi-organ segmentation show that our proposed OARFocalFuseNet outperforms the recent state-of-the-art methods on publicly available OpenKBP datasets and Synapse multi-organ segmentation. Both of the proposed methods (3D-MSF and OARFocalFuseNet) showed promising performance in terms of standard evaluation metrics. Our best performing method (OARFocalFuseNet) obtained a dice coefficient of 0.7995 and hausdorff distance of 5.1435 on OpenKBP datasets and dice coefficient of 0.8137 on Synapse multi-organ segmentation dataset. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.07417v1-abstract-full').style.display = 'none'; document.getElementById('2208.07417v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 August, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">4 pages, 2 figures, 2 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2205.12162">arXiv:2205.12162</a> <span> [<a href="https://arxiv.org/pdf/2205.12162">pdf</a>, <a href="https://arxiv.org/format/2205.12162">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Earth and Planetary Astrophysics">astro-ph.EP</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.3847/1538-3881/acb7e2">10.3847/1538-3881/acb7e2 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> The PEPSI Exoplanet Transit Survey (PETS). II. A Deep Search for Thermal Inversion Agents in KELT-20 b/MASCARA-2 b with Emission and Transmission Spectroscopy </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Johnson%2C+M+C">Marshall C. Johnson</a>, <a href="/search/?searchtype=author&query=Wang%2C+J">Ji Wang</a>, <a href="/search/?searchtype=author&query=Asnodkar%2C+A+P">Anusha Pai Asnodkar</a>, <a href="/search/?searchtype=author&query=Bonomo%2C+A+S">Aldo S. Bonomo</a>, <a href="/search/?searchtype=author&query=Gaudi%2C+B+S">B. Scott Gaudi</a>, <a href="/search/?searchtype=author&query=Henning%2C+T">Thomas Henning</a>, <a href="/search/?searchtype=author&query=Ilyin%2C+I">Ilya Ilyin</a>, <a href="/search/?searchtype=author&query=Keles%2C+E">Engin Keles</a>, <a href="/search/?searchtype=author&query=Malavolta%2C+L">Luca Malavolta</a>, <a href="/search/?searchtype=author&query=Mallonn%2C+M">Matthias Mallonn</a>, <a href="/search/?searchtype=author&query=Molaverdikhani%2C+K">Karan Molaverdikhani</a>, <a href="/search/?searchtype=author&query=Nascimbeni%2C+V">Valerio Nascimbeni</a>, <a href="/search/?searchtype=author&query=Patience%2C+J">Jennifer Patience</a>, <a href="/search/?searchtype=author&query=Poppenhaeger%2C+K">Katja Poppenhaeger</a>, <a href="/search/?searchtype=author&query=Scandariato%2C+G">Gaetano Scandariato</a>, <a href="/search/?searchtype=author&query=Schlawin%2C+E">Everett Schlawin</a>, <a href="/search/?searchtype=author&query=Shkolnik%2C+E">Evgenya Shkolnik</a>, <a href="/search/?searchtype=author&query=Sicilia%2C+D">Daniela Sicilia</a>, <a href="/search/?searchtype=author&query=Sozzetti%2C+A">Alessandro Sozzetti</a>, <a href="/search/?searchtype=author&query=Strassmeier%2C+K+G">Klaus G. Strassmeier</a>, <a href="/search/?searchtype=author&query=Veillet%2C+C">Christian Veillet</a>, <a href="/search/?searchtype=author&query=Yan%2C+F">Fei Yan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2205.12162v2-abstract-short" style="display: inline;"> Recent observations have shown that the atmospheres of ultra hot Jupiters (UHJs) commonly possess temperature inversions, where the temperature increases with increasing altitude. Nonetheless, which opacity sources are responsible for the presence of these inversions remains largely observationally unconstrained. We used LBT/PEPSI to observe the atmosphere of the UHJ KELT-20 b in both transmission… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.12162v2-abstract-full').style.display = 'inline'; document.getElementById('2205.12162v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2205.12162v2-abstract-full" style="display: none;"> Recent observations have shown that the atmospheres of ultra hot Jupiters (UHJs) commonly possess temperature inversions, where the temperature increases with increasing altitude. Nonetheless, which opacity sources are responsible for the presence of these inversions remains largely observationally unconstrained. We used LBT/PEPSI to observe the atmosphere of the UHJ KELT-20 b in both transmission and emission in order to search for molecular agents which could be responsible for the temperature inversion. We validate our methodology by confirming previous detections of Fe I in emission at $16.9蟽$. Our search for the inversion agents TiO, VO, FeH, and CaH results in non-detections. Using injection-recovery testing we set $4蟽$ upper limits upon the volume mixing ratios for these constituents as low as $\sim1\times10^{-9}$ for TiO. For TiO, VO, and CaH, our limits are much lower than expectations from an equilibrium chemical model, while we cannot set constraining limits on FeH with our data. We thus rule out TiO and CaH as the source of the temperature inversion in KELT-20 b, and VO only if the line lists are sufficiently accurate. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.12162v2-abstract-full').style.display = 'none'; document.getElementById('2205.12162v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 31 January, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 May, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">18 pages, 11 figures. Accepted for publication in AJ</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2205.10663">arXiv:2205.10663</a> <span> [<a href="https://arxiv.org/pdf/2205.10663">pdf</a>, <a href="https://arxiv.org/format/2205.10663">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Transformer based Generative Adversarial Network for Liver Segmentation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Demir%2C+U">Ugur Demir</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Z">Zheyuan Zhang</a>, <a href="/search/?searchtype=author&query=Wang%2C+B">Bin Wang</a>, <a href="/search/?searchtype=author&query=Antalek%2C+M">Matthew Antalek</a>, <a href="/search/?searchtype=author&query=Keles%2C+E">Elif Keles</a>, <a href="/search/?searchtype=author&query=Jha%2C+D">Debesh Jha</a>, <a href="/search/?searchtype=author&query=Borhani%2C+A">Amir Borhani</a>, <a href="/search/?searchtype=author&query=Ladner%2C+D">Daniela Ladner</a>, <a href="/search/?searchtype=author&query=Bagci%2C+U">Ulas Bagci</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2205.10663v2-abstract-short" style="display: inline;"> Automated liver segmentation from radiology scans (CT, MRI) can improve surgery and therapy planning and follow-up assessment in addition to conventional use for diagnosis and prognosis. Although convolutional neural networks (CNNs) have become the standard image segmentation tasks, more recently this has started to change towards Transformers based architectures because Transformers are taking ad… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.10663v2-abstract-full').style.display = 'inline'; document.getElementById('2205.10663v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2205.10663v2-abstract-full" style="display: none;"> Automated liver segmentation from radiology scans (CT, MRI) can improve surgery and therapy planning and follow-up assessment in addition to conventional use for diagnosis and prognosis. Although convolutional neural networks (CNNs) have become the standard image segmentation tasks, more recently this has started to change towards Transformers based architectures because Transformers are taking advantage of capturing long range dependence modeling capability in signals, so called attention mechanism. In this study, we propose a new segmentation approach using a hybrid approach combining the Transformer(s) with the Generative Adversarial Network (GAN) approach. The premise behind this choice is that the self-attention mechanism of the Transformers allows the network to aggregate the high dimensional feature and provide global information modeling. This mechanism provides better segmentation performance compared with traditional methods. Furthermore, we encode this generator into the GAN based architecture so that the discriminator network in the GAN can classify the credibility of the generated segmentation masks compared with the real masks coming from human (expert) annotations. This allows us to extract the high dimensional topology information in the mask for biomedical image segmentation and provide more reliable segmentation results. Our model achieved a high dice coefficient of 0.9433, recall of 0.9515, and precision of 0.9376 and outperformed other Transformer based approaches. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.10663v2-abstract-full').style.display = 'none'; document.getElementById('2205.10663v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 May, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 May, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> ICPAI 2021 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2203.16856">arXiv:2203.16856</a> <span> [<a href="https://arxiv.org/pdf/2203.16856">pdf</a>, <a href="https://arxiv.org/ps/2203.16856">ps</a>, <a href="https://arxiv.org/format/2203.16856">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Earth and Planetary Astrophysics">astro-ph.EP</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1093/mnras/stac810">10.1093/mnras/stac810 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> The PEPSI Exoplanet Transit Survey (PETS) I: Investigating the presence of a silicate atmosphere on the super-Earth 55 Cnc e </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Keles%2C+E">Engin Keles</a>, <a href="/search/?searchtype=author&query=Mallonn%2C+M">Matthias Mallonn</a>, <a href="/search/?searchtype=author&query=Kitzmann%2C+D">Daniel Kitzmann</a>, <a href="/search/?searchtype=author&query=Poppenhaeger%2C+K">Katja Poppenhaeger</a>, <a href="/search/?searchtype=author&query=Hoeijmakers%2C+H+J">H. Jens Hoeijmakers</a>, <a href="/search/?searchtype=author&query=Ilyin%2C+I">Ilya Ilyin</a>, <a href="/search/?searchtype=author&query=Alexoudi%2C+X">Xanthippi Alexoudi</a>, <a href="/search/?searchtype=author&query=Carroll%2C+T+A">Thorsten A. Carroll</a>, <a href="/search/?searchtype=author&query=Alvarado-Gomez%2C+J">Julian Alvarado-Gomez</a>, <a href="/search/?searchtype=author&query=Ketzer%2C+L">Laura Ketzer</a>, <a href="/search/?searchtype=author&query=Bonomo%2C+A+S">Aldo S. Bonomo</a>, <a href="/search/?searchtype=author&query=Borsa%2C+F">Francesco Borsa</a>, <a href="/search/?searchtype=author&query=Gaudi%2C+S">Scott Gaudi</a>, <a href="/search/?searchtype=author&query=Henning%2C+T">Thomas Henning</a>, <a href="/search/?searchtype=author&query=Malavolta%2C+L">Luca Malavolta</a>, <a href="/search/?searchtype=author&query=Molaverdikhani%2C+K">Karan Molaverdikhani</a>, <a href="/search/?searchtype=author&query=Nascimbeni%2C+V">Valerio Nascimbeni</a>, <a href="/search/?searchtype=author&query=Patience%2C+J">Jennifer Patience</a>, <a href="/search/?searchtype=author&query=Pino%2C+L">Lorenzo Pino</a>, <a href="/search/?searchtype=author&query=Scandariato%2C+G">Gaetano Scandariato</a>, <a href="/search/?searchtype=author&query=Schlawin%2C+E">Everett Schlawin</a>, <a href="/search/?searchtype=author&query=Shkolnik%2C+E">Evgenya Shkolnik</a>, <a href="/search/?searchtype=author&query=Sicilia%2C+D">Daniela Sicilia</a>, <a href="/search/?searchtype=author&query=Sozzetti%2C+A">Alessandro Sozzetti</a>, <a href="/search/?searchtype=author&query=Foster%2C+M+G">Mary G. Foster</a> , et al. (4 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2203.16856v1-abstract-short" style="display: inline;"> The study of exoplanets and especially their atmospheres can reveal key insights on their evolution by identifying specific atmospheric species. For such atmospheric investigations, high-resolution transmission spectroscopy has shown great success, especially for Jupiter-type planets. Towards the atmospheric characterization of smaller planets, the super-Earth exoplanet 55 Cnc e is one of the most… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.16856v1-abstract-full').style.display = 'inline'; document.getElementById('2203.16856v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2203.16856v1-abstract-full" style="display: none;"> The study of exoplanets and especially their atmospheres can reveal key insights on their evolution by identifying specific atmospheric species. For such atmospheric investigations, high-resolution transmission spectroscopy has shown great success, especially for Jupiter-type planets. Towards the atmospheric characterization of smaller planets, the super-Earth exoplanet 55 Cnc e is one of the most promising terrestrial exoplanets studied to date. Here, we present a high-resolution spectroscopic transit observation of this planet, acquired with the PEPSI instrument at the Large Binocular Telescope. Assuming the presence of Earth-like crust species on the surface of 55 Cnc e, from which a possible silicate-vapor atmosphere could have originated, we search in its transmission spectrum for absorption of various atomic and ionized species such as Fe , Fe+, Ca , Ca+, Mg and K , among others. Not finding absorption for any of the investigated species, we are able to set absorption limits with a median value of 1.9 x RP. In conclusion, we do not find evidence of a widely extended silicate envelope on this super-Earth reaching several planetary radii. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.16856v1-abstract-full').style.display = 'none'; document.getElementById('2203.16856v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 31 March, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">MNRAS, in press</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2104.02869">arXiv:2104.02869</a> <span> [<a href="https://arxiv.org/pdf/2104.02869">pdf</a>, <a href="https://arxiv.org/format/2104.02869">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Information Bottleneck Attribution for Visual Explanations of Diagnosis and Prognosis </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Demir%2C+U">Ugur Demir</a>, <a href="/search/?searchtype=author&query=Irmakci%2C+I">Ismail Irmakci</a>, <a href="/search/?searchtype=author&query=Keles%2C+E">Elif Keles</a>, <a href="/search/?searchtype=author&query=Topcu%2C+A">Ahmet Topcu</a>, <a href="/search/?searchtype=author&query=Xu%2C+Z">Ziyue Xu</a>, <a href="/search/?searchtype=author&query=Spampinato%2C+C">Concetto Spampinato</a>, <a href="/search/?searchtype=author&query=Jambawalikar%2C+S">Sachin Jambawalikar</a>, <a href="/search/?searchtype=author&query=Turkbey%2C+E">Evrim Turkbey</a>, <a href="/search/?searchtype=author&query=Turkbey%2C+B">Baris Turkbey</a>, <a href="/search/?searchtype=author&query=Bagci%2C+U">Ulas Bagci</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2104.02869v2-abstract-short" style="display: inline;"> Visual explanation methods have an important role in the prognosis of the patients where the annotated data is limited or unavailable. There have been several attempts to use gradient-based attribution methods to localize pathology from medical scans without using segmentation labels. This research direction has been impeded by the lack of robustness and reliability. These methods are highly sensi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2104.02869v2-abstract-full').style.display = 'inline'; document.getElementById('2104.02869v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2104.02869v2-abstract-full" style="display: none;"> Visual explanation methods have an important role in the prognosis of the patients where the annotated data is limited or unavailable. There have been several attempts to use gradient-based attribution methods to localize pathology from medical scans without using segmentation labels. This research direction has been impeded by the lack of robustness and reliability. These methods are highly sensitive to the network parameters. In this study, we introduce a robust visual explanation method to address this problem for medical applications. We provide an innovative visual explanation algorithm for general purpose and as an example application, we demonstrate its effectiveness for quantifying lesions in the lungs caused by the Covid-19 with high accuracy and robustness without using dense segmentation labels. This approach overcomes the drawbacks of commonly used Grad-CAM and its extended versions. The premise behind our proposed strategy is that the information flow is minimized while ensuring the classifier prediction stays similar. Our findings indicate that the bottleneck condition provides a more stable severity estimation than the similar attribution methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2104.02869v2-abstract-full').style.display = 'none'; document.getElementById('2104.02869v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 June, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 April, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2101.07143">arXiv:2101.07143</a> <span> [<a href="https://arxiv.org/pdf/2101.07143">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Earth and Planetary Astrophysics">astro-ph.EP</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1093/mnras/stab099">10.1093/mnras/stab099 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Spectral signature of atmospheric winds in high resolution transit observations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Keles%2C+E">Engin Keles</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2101.07143v2-abstract-short" style="display: inline;"> The study of exoplanet atmospheres showed large diversity compared to the planets in our solar system. Especially Jupiter type exoplanets orbiting their host star in close orbits, the so-called hot and ultra-hot Jupiters, have been studied in detail due to their enhanced atmospheric signature. Due to their tidally locked status, the temperature difference between the day- and nightside triggers at… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.07143v2-abstract-full').style.display = 'inline'; document.getElementById('2101.07143v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2101.07143v2-abstract-full" style="display: none;"> The study of exoplanet atmospheres showed large diversity compared to the planets in our solar system. Especially Jupiter type exoplanets orbiting their host star in close orbits, the so-called hot and ultra-hot Jupiters, have been studied in detail due to their enhanced atmospheric signature. Due to their tidally locked status, the temperature difference between the day- and nightside triggers atmospheric winds which can lead to various fingerprints in the observations. Spatially resolved absorption lines during transit such as sodium (Na) could be a good tracer for such winds. Different works resolved the Na$^-$ absorption lines on different exoplanets which show different line widths. Assuming that this could be attributed to such zonal jet streams, this work models the effect of such winds on synthetic absorption lines. For this, transiting Jupiter type planets with rotational velocities similar to hot and ultra-hot Jupiter are considered. The investigation shows that high wind velocities could reproduce the broadening of Na-line profiles inferred in different high-resolution transit observations. There is a tendency that the broadening values decrease for planets with lower equilibrium temperature. This could be explained by atmospheric drag induced by the ionization of alkali lines which slow down the zonal jet streams, favoring their existence on hot Jupiter rather than ultra-hot Jupiter. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.07143v2-abstract-full').style.display = 'none'; document.getElementById('2101.07143v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 February, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 18 January, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted for publication</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2008.04044">arXiv:2008.04044</a> <span> [<a href="https://arxiv.org/pdf/2008.04044">pdf</a>, <a href="https://arxiv.org/format/2008.04044">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Earth and Planetary Astrophysics">astro-ph.EP</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1093/mnras/staa2435">10.1093/mnras/staa2435 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Probing the atmosphere of HD189733b with the Na I and K I lines </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Keles%2C+E">E. Keles</a>, <a href="/search/?searchtype=author&query=Kitzmann%2C+D">D. Kitzmann</a>, <a href="/search/?searchtype=author&query=Mallonn%2C+M">M. Mallonn</a>, <a href="/search/?searchtype=author&query=Alexoudi%2C+X">X. Alexoudi</a>, <a href="/search/?searchtype=author&query=Fossati%2C+L">L. Fossati</a>, <a href="/search/?searchtype=author&query=Pino%2C+L">L. Pino</a>, <a href="/search/?searchtype=author&query=Seidel%2C+J+V">J. V. Seidel</a>, <a href="/search/?searchtype=author&query=Carroll%2C+T+A">T. A. Carroll</a>, <a href="/search/?searchtype=author&query=Steffen%2C+M">M. Steffen</a>, <a href="/search/?searchtype=author&query=Ilyin%2C+I">I. Ilyin</a>, <a href="/search/?searchtype=author&query=Poppenhaeger%2C+K">K. Poppenhaeger</a>, <a href="/search/?searchtype=author&query=Strassmeier%2C+K+G">K. G. Strassmeier</a>, <a href="/search/?searchtype=author&query=von+Essen%2C+C">C. von Essen</a>, <a href="/search/?searchtype=author&query=Nascimbeni%2C+V">V. Nascimbeni</a>, <a href="/search/?searchtype=author&query=Turner%2C+J+D">J. D. Turner</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2008.04044v1-abstract-short" style="display: inline;"> High spectral resolution transmission spectroscopy is a powerful tool to characterize exoplanet atmospheres. Especially for hot Jupiters, this technique is highly relevant, due to their high altitude absorption e.g. from resonant sodium (Na I) and potassium (K I) lines. We resolve the atmospheric K I-absorption on HD189733b with the aim to compare the resolved K I -line and previously obtained hig… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.04044v1-abstract-full').style.display = 'inline'; document.getElementById('2008.04044v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2008.04044v1-abstract-full" style="display: none;"> High spectral resolution transmission spectroscopy is a powerful tool to characterize exoplanet atmospheres. Especially for hot Jupiters, this technique is highly relevant, due to their high altitude absorption e.g. from resonant sodium (Na I) and potassium (K I) lines. We resolve the atmospheric K I-absorption on HD189733b with the aim to compare the resolved K I -line and previously obtained high resolution Na I-D-line observations with synthetic transmission spectra. The line profiles suggest atmospheric processes leading to a line broadening of the order of 10 km/s for the Na I-D-lines, and only a few km/s for the K I-line. The investigation hints that either the atmosphere of HD189733b lacks a significant amount of K I or the alkali lines probe different atmospheric regions with different temperature, which could explain the differences we see in the resolved absorption lines. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.04044v1-abstract-full').style.display = 'none'; document.getElementById('2008.04044v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 August, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2007.07716">arXiv:2007.07716</a> <span> [<a href="https://arxiv.org/pdf/2007.07716">pdf</a>, <a href="https://arxiv.org/format/2007.07716">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Earth and Planetary Astrophysics">astro-ph.EP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Instrumentation and Methods for Astrophysics">astro-ph.IM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Solar and Stellar Astrophysics">astro-ph.SR</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1051/0004-6361/202038080">10.1051/0004-6361/202038080 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Role of the impact parameter in exoplanet transmission spectroscopy </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Alexoudi%2C+X">X. Alexoudi</a>, <a href="/search/?searchtype=author&query=Mallonn%2C+M">M. Mallonn</a>, <a href="/search/?searchtype=author&query=Keles%2C+E">E. Keles</a>, <a href="/search/?searchtype=author&query=Poppenhaeger%2C+K">K. Poppenhaeger</a>, <a href="/search/?searchtype=author&query=von+Essen%2C+C">C. von Essen</a>, <a href="/search/?searchtype=author&query=Strassmeier%2C+K+G">K. G. Strassmeier</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2007.07716v1-abstract-short" style="display: inline;"> Transmission spectroscopy is a promising tool for the atmospheric characterization of transiting exoplanets. Because the planetary signal is faint, discrepancies have been reported regarding individual targets. We investigate the dependence of the estimated transmission spectrum on deviations of the orbital parameters of the star-planet system that are due to the limb-darkening effects of the host… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.07716v1-abstract-full').style.display = 'inline'; document.getElementById('2007.07716v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2007.07716v1-abstract-full" style="display: none;"> Transmission spectroscopy is a promising tool for the atmospheric characterization of transiting exoplanets. Because the planetary signal is faint, discrepancies have been reported regarding individual targets. We investigate the dependence of the estimated transmission spectrum on deviations of the orbital parameters of the star-planet system that are due to the limb-darkening effects of the host star. We describe how the uncertainty on the orbital parameters translates into an uncertainty on the planetary spectral slope. We created synthetic transit light curves in seven different wavelength bands, from the near-ultraviolet to the near-infrared, and fit them with transit models parameterized by fixed deviating values of the impact parameter $b$. Our simulations show a wavelength-dependent offset that is more pronounced at the blue wavelengths where the limb-darkening effect is stronger. This offset introduces a slope in the planetary transmission spectrum that becomes steeper with increasing $b$ values. Variations of $b$ by positive or negative values within its uncertainty interval introduce positive or negative slopes, thus the formation of an error envelope. The amplitude from blue optical to near-infrared wavelength for a typical uncertainty on $b$ corresponds to one atmospheric pressure scale height and more. This impact parameter degeneracy is confirmed for different host types; K stars present prominently steeper slopes, while M stars indicate features at the blue wavelengths. We demonstrate that transmission spectra can be hard to interpret, basically because of the limitations in defining a precise impact parameter value for a transiting exoplanet. This consequently limits a characterization of its atmosphere. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.07716v1-abstract-full').style.display = 'none'; document.getElementById('2007.07716v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 July, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> A&A 640, A134 (2020) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2006.05207">arXiv:2006.05207</a> <span> [<a href="https://arxiv.org/pdf/2006.05207">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Earth and Planetary Astrophysics">astro-ph.EP</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1089/ast.2016.1632">10.1089/ast.2016.1632 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> The effect of varying atmospheric pressure upon habitability and biosignatures of Earth-like planets </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Keles%2C+E">Engin Keles</a>, <a href="/search/?searchtype=author&query=Grenfell%2C+J+L">John Lee Grenfell</a>, <a href="/search/?searchtype=author&query=Godolt%2C+M">Mareike Godolt</a>, <a href="/search/?searchtype=author&query=Stracke%2C+B">Barbara Stracke</a>, <a href="/search/?searchtype=author&query=Rauer%2C+H">Heike Rauer</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2006.05207v1-abstract-short" style="display: inline;"> Understanding the possible climatic conditions on rocky extrasolar planets, and thereby their potential habitability, is one of the major subjects of exoplanet research. Determining how the climate, as well as potential atmospheric biosignatures, change under different conditions is a key aspect when studying Earth-like exoplanets. One important property is the atmospheric mass hence pressure and… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.05207v1-abstract-full').style.display = 'inline'; document.getElementById('2006.05207v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2006.05207v1-abstract-full" style="display: none;"> Understanding the possible climatic conditions on rocky extrasolar planets, and thereby their potential habitability, is one of the major subjects of exoplanet research. Determining how the climate, as well as potential atmospheric biosignatures, change under different conditions is a key aspect when studying Earth-like exoplanets. One important property is the atmospheric mass hence pressure and its influence on the climatic conditions. Therefore, the aim of the present study is to understand the influence of atmospheric mass on climate, hence habitability, and the spectral appearance of planets with Earth-like, that is, N2-O2 dominated, atmospheres orbiting the Sun at 1 Astronomical Unit. This work utilizes a 1D coupled, cloud-free, climate-photochemical atmospheric column model; varies atmospheric surface pressure from 0.5 bar to 30 bar; and investigates temperature and key species profiles, as well as emission and brightness temperature spectra in a range between 2渭m - 20渭m. Increasing the surface pressure up to 4 bar leads to an increase in the surface temperature due to increased greenhouse warming. Above this point, Rayleigh scattering dominates and the surface temperature decreases, reaching surface temperatures below 273K (approximately at ~34 bar surface pressure). For ozone, nitrous oxide, water, methane, and carbon dioxide, the spectral response either increases with surface temperature or pressure depending on the species. Masking effects occur, for example, for the bands of the biosignatures ozone and nitrous oxide by carbon dioxide, which could be visible in low carbon dioxide atmospheres. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.05207v1-abstract-full').style.display = 'none'; document.getElementById('2006.05207v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 June, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Astrobiology, Volume 18, Issue 2, 2018, pp.116-132 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2002.08690">arXiv:2002.08690</a> <span> [<a href="https://arxiv.org/pdf/2002.08690">pdf</a>, <a href="https://arxiv.org/format/2002.08690">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Earth and Planetary Astrophysics">astro-ph.EP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Instrumentation and Methods for Astrophysics">astro-ph.IM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Solar and Stellar Astrophysics">astro-ph.SR</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1051/0004-6361/201936091">10.1051/0004-6361/201936091 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> High-resolution spectroscopy and spectropolarimetry of the total lunar eclipse January 2019 </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Strassmeier%2C+K+G">K. G. Strassmeier</a>, <a href="/search/?searchtype=author&query=Ilyin%2C+I">I. Ilyin</a>, <a href="/search/?searchtype=author&query=Keles%2C+E">E. Keles</a>, <a href="/search/?searchtype=author&query=Mallonn%2C+M">M. Mallonn</a>, <a href="/search/?searchtype=author&query=J%C3%A4rvinen%2C+A">A. J盲rvinen</a>, <a href="/search/?searchtype=author&query=Weber%2C+M">M. Weber</a>, <a href="/search/?searchtype=author&query=Mackebrandt%2C+F">F. Mackebrandt</a>, <a href="/search/?searchtype=author&query=Hill%2C+J+M">J. M. Hill</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2002.08690v1-abstract-short" style="display: inline;"> Observations of the Earthshine off the Moon allow for the unique opportunity to measure the large-scale Earth atmosphere. Another opportunity is realized during a total lunar eclipse which, if seen from the Moon, is like a transit of the Earth in front of the Sun. We thus aim at transmission spectroscopy of an Earth transit by tracing the solar spectrum during the total lunar eclipse of January 21… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.08690v1-abstract-full').style.display = 'inline'; document.getElementById('2002.08690v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2002.08690v1-abstract-full" style="display: none;"> Observations of the Earthshine off the Moon allow for the unique opportunity to measure the large-scale Earth atmosphere. Another opportunity is realized during a total lunar eclipse which, if seen from the Moon, is like a transit of the Earth in front of the Sun. We thus aim at transmission spectroscopy of an Earth transit by tracing the solar spectrum during the total lunar eclipse of January 21, 2019. Time series spectra of the Tycho crater were taken with the Potsdam Echelle Polarimetric and Spectroscopic Instrument (PEPSI) at the Large Binocular Telescope (LBT) in its polarimetric mode in Stokes IQUV at a spectral resolution of 130000 (0.06 脜). In particular, the spectra cover the red parts of the optical spectrum between 7419-9067 脜. The spectrograph's exposure meter was used to obtain a light curve of the lunar eclipse. The brightness of the Moon dimmed by 10.75 mag during umbral eclipse. We found both branches of the O$_2$ A-band almost completely saturated as well as a strong increase of H$_2$O absorption during totality. The deep penumbral spectra show significant excess absorption from the NaI 5890 脜doublet, the CaII infrared triplet around 8600 脜, and the KI line at 7699 脜in addition to several hyper-fine-structure lines of MnI and even from BaII. The detections of the latter two elements are likely due to an untypical solar center-to-limb effect rather than Earth's atmosphere. The absorption in CaII and KI remained visible throughout umbral eclipse. A small continuum polarization of the O$_2$ A-band of 0.12\% during umbral eclipse was detected at 6.3$蟽$. No line polarization of the O$_2$ A-band, or any other spectral-line feature, is detected outside nor inside eclipse. It places an upper limit of $\approx$0.2\% on the degree of line polarization during transmission through Earth's atmosphere and magnetosphere. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.08690v1-abstract-full').style.display = 'none'; document.getElementById('2002.08690v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 February, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted for publication in Astronomy & Astrophysics</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> A&A 635, A156 (2020) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1909.04884">arXiv:1909.04884</a> <span> [<a href="https://arxiv.org/pdf/1909.04884">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Earth and Planetary Astrophysics">astro-ph.EP</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1093/mnrasl/slz123">10.1093/mnrasl/slz123 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> The potassium absorption on HD189733b and HD209458b </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Keles%2C+E">Engin Keles</a>, <a href="/search/?searchtype=author&query=Mallonn%2C+M">Matthias Mallonn</a>, <a href="/search/?searchtype=author&query=von+Essen%2C+C">Carolina von Essen</a>, <a href="/search/?searchtype=author&query=Carroll%2C+T+A">Thorsten A. Carroll</a>, <a href="/search/?searchtype=author&query=Alexoudi%2C+X">Xanthippi Alexoudi</a>, <a href="/search/?searchtype=author&query=Pino%2C+L">Lorenzo Pino</a>, <a href="/search/?searchtype=author&query=Ilyin%2C+I">Ilya Ilyin</a>, <a href="/search/?searchtype=author&query=Poppenhaeger%2C+K">Katja Poppenhaeger</a>, <a href="/search/?searchtype=author&query=Kitzmann%2C+D">Daniel Kitzmann</a>, <a href="/search/?searchtype=author&query=Nascimbeni%2C+V">Valerio Nascimbeni</a>, <a href="/search/?searchtype=author&query=Turner%2C+J">Jake Turner</a>, <a href="/search/?searchtype=author&query=Strassmeier%2C+K+G">Klaus G. Strassmeier</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1909.04884v2-abstract-short" style="display: inline;"> In this work, we investigate the potassium excess absorption around 7699A of the exoplanets HD189733b and HD209458b. For this purpose, we used high spectral resolution transit observations acquired with the 2 x 8.4m Large Binocular Telescope (LBT) and the Potsdam Echelle Polarimetric and Spectroscopic Instrument (PEPSI). For a bandwidth of 0.8A, we present a detection > 7-sigma with an absorption… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1909.04884v2-abstract-full').style.display = 'inline'; document.getElementById('1909.04884v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1909.04884v2-abstract-full" style="display: none;"> In this work, we investigate the potassium excess absorption around 7699A of the exoplanets HD189733b and HD209458b. For this purpose, we used high spectral resolution transit observations acquired with the 2 x 8.4m Large Binocular Telescope (LBT) and the Potsdam Echelle Polarimetric and Spectroscopic Instrument (PEPSI). For a bandwidth of 0.8A, we present a detection > 7-sigma with an absorption level of 0.18% for HD189733b. Applying the same analysis to HD209458b, we can set 3-sigma upper limit of 0.09%, even though we do not detect a K- excess absorption. The investigation suggests that the K- feature is less present in the atmosphere of HD209458b than in the one of HD189733b. This comparison confirms previous claims that the atmospheres of these two planets must have fundamentally different properties. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1909.04884v2-abstract-full').style.display = 'none'; document.getElementById('1909.04884v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 September, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 September, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted in M.N.R.A.S, https://doi.org/10.1093/mnrasl/slz123</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1812.05882">arXiv:1812.05882</a> <span> [<a href="https://arxiv.org/pdf/1812.05882">pdf</a>, <a href="https://arxiv.org/ps/1812.05882">ps</a>, <a href="https://arxiv.org/format/1812.05882">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Earth and Planetary Astrophysics">astro-ph.EP</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1051/0004-6361/201834194">10.1051/0004-6361/201834194 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Ephemeris refinement of 21 Hot Jupiter exoplanets with high timing uncertainties </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Mallonn%2C+M">M. Mallonn</a>, <a href="/search/?searchtype=author&query=von+Essen%2C+C">C. von Essen</a>, <a href="/search/?searchtype=author&query=Herrero%2C+E">E. Herrero</a>, <a href="/search/?searchtype=author&query=Alexoudi%2C+X">X. Alexoudi</a>, <a href="/search/?searchtype=author&query=Granzer%2C+T">T. Granzer</a>, <a href="/search/?searchtype=author&query=Sosa%2C+M">M. Sosa</a>, <a href="/search/?searchtype=author&query=Strassmeier%2C+K+G">K. G. Strassmeier</a>, <a href="/search/?searchtype=author&query=Bakos%2C+G">G. Bakos</a>, <a href="/search/?searchtype=author&query=Bayliss%2C+D">D. Bayliss</a>, <a href="/search/?searchtype=author&query=Brahm%2C+R">R. Brahm</a>, <a href="/search/?searchtype=author&query=Bretton%2C+M">M. Bretton</a>, <a href="/search/?searchtype=author&query=Campos%2C+F">F. Campos</a>, <a href="/search/?searchtype=author&query=Carone%2C+L">L. Carone</a>, <a href="/search/?searchtype=author&query=Col%C3%B3n%2C+K+D">K. D. Col贸n</a>, <a href="/search/?searchtype=author&query=Dale%2C+H+A">H. A. Dale</a>, <a href="/search/?searchtype=author&query=Dragomir%2C+D">D. Dragomir</a>, <a href="/search/?searchtype=author&query=Espinoza%2C+N">N. Espinoza</a>, <a href="/search/?searchtype=author&query=Evans%2C+P">P. Evans</a>, <a href="/search/?searchtype=author&query=Garcia%2C+F">F. Garcia</a>, <a href="/search/?searchtype=author&query=Gu%2C+S+-">S. -H. Gu</a>, <a href="/search/?searchtype=author&query=Guerra%2C+P">P. Guerra</a>, <a href="/search/?searchtype=author&query=Jongen%2C+Y">Y. Jongen</a>, <a href="/search/?searchtype=author&query=Jord%C3%A1n%2C+A">A. Jord谩n</a>, <a href="/search/?searchtype=author&query=Kang%2C+W">W. Kang</a>, <a href="/search/?searchtype=author&query=Keles%2C+E">E. Keles</a> , et al. (10 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1812.05882v2-abstract-short" style="display: inline;"> Transit events of extrasolar planets offer a wealth of information for planetary characterization. However, for many known targets, the uncertainty of their predicted transit windows prohibits an accurate scheduling of follow-up observations. In this work, we refine the ephemerides of 21 Hot Jupiter exoplanets with the largest timing uncertainty. We collected 120 professional and amateur transit l… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1812.05882v2-abstract-full').style.display = 'inline'; document.getElementById('1812.05882v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1812.05882v2-abstract-full" style="display: none;"> Transit events of extrasolar planets offer a wealth of information for planetary characterization. However, for many known targets, the uncertainty of their predicted transit windows prohibits an accurate scheduling of follow-up observations. In this work, we refine the ephemerides of 21 Hot Jupiter exoplanets with the largest timing uncertainty. We collected 120 professional and amateur transit light curves of the targets of interest, observed with 0.3m to 2.2m telescopes, and analyzed them including the timing information of the planets discovery papers. In the case of WASP-117b, we measured a timing deviation compared to the known ephemeris of about 3.5 hours, for HAT-P-29b and HAT-P-31b the deviation amounted to about 2 hours and more. For all targets, the new ephemeris predicts transit timings with uncertainties of less than 6 minutes in the year 2018 and less than 13 minutes until 2025. Thus, our results allow for an accurate scheduling of follow-up observations in the next decade. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1812.05882v2-abstract-full').style.display = 'none'; document.getElementById('1812.05882v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 January, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 December, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2018. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">accepted for publication in Astronomy & Astrophysics</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> A&A 622, A81 (2019) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1810.02172">arXiv:1810.02172</a> <span> [<a href="https://arxiv.org/pdf/1810.02172">pdf</a>, <a href="https://arxiv.org/format/1810.02172">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Earth and Planetary Astrophysics">astro-ph.EP</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1051/0004-6361/201833691">10.1051/0004-6361/201833691 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Deciphering the atmosphere of HAT-P-12b: solving discrepant results </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Alexoudi%2C+X">X. Alexoudi</a>, <a href="/search/?searchtype=author&query=Mallonn%2C+M">M. Mallonn</a>, <a href="/search/?searchtype=author&query=von+Essen%2C+C">C. von Essen</a>, <a href="/search/?searchtype=author&query=Turner%2C+J+D">J. D. Turner</a>, <a href="/search/?searchtype=author&query=Keles%2C+E">E. Keles</a>, <a href="/search/?searchtype=author&query=Southworth%2C+J">J. Southworth</a>, <a href="/search/?searchtype=author&query=Mancini%2C+L">L. Mancini</a>, <a href="/search/?searchtype=author&query=Ciceri%2C+S">S. Ciceri</a>, <a href="/search/?searchtype=author&query=Granzer%2C+T">T. Granzer</a>, <a href="/search/?searchtype=author&query=Denker%2C+C">C. Denker</a>, <a href="/search/?searchtype=author&query=Dineva%2C+E">E. Dineva</a>, <a href="/search/?searchtype=author&query=Strassmeier%2C+K+G">K. G. Strassmeier</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1810.02172v2-abstract-short" style="display: inline;"> Two independent investigations of the atmosphere of the hot Jupiter HAT-P-12b by two different groups resulted in discrepant solutions. Using broad-band photometry from the ground, one study found a flat and featureless transmission spectrum which was interpreted as a gray absorption by dense cloud coverage. The second study made use of the Hubble Space Telescope (HST) observations and found Rayle… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1810.02172v2-abstract-full').style.display = 'inline'; document.getElementById('1810.02172v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1810.02172v2-abstract-full" style="display: none;"> Two independent investigations of the atmosphere of the hot Jupiter HAT-P-12b by two different groups resulted in discrepant solutions. Using broad-band photometry from the ground, one study found a flat and featureless transmission spectrum which was interpreted as a gray absorption by dense cloud coverage. The second study made use of the Hubble Space Telescope (HST) observations and found Rayleigh scattering at optical wavelengths caused by haze. The main purpose of this work is to find the source of this inconsistency and provide feedback to prevent similar discrepancies in future analyses of other exoplanetary atmospheres. We studied the observed discrepancy via two methods. With further broad-band observations in the optical wavelength regions, we strengthened the previous measurements in precision and with a homogeneous reanalysis of the published data, we managed to assess the systematic errors and the independent analyses of the two different groups. Repeating the analysis steps of both works, we found that deviating values for the orbital parameters are the reason for the aforementioned discrepancy. Our work showed a degeneracy of the planetary spectral slope with these parameters. In a homogeneous reanalysis of all data, the two literature data sets and the new observations converge to a consistent transmission spectrum, showing a low-amplitude spectral slope and a tentative detection of potassium absorption. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1810.02172v2-abstract-full').style.display = 'none'; document.getElementById('1810.02172v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 December, 2018; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 October, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2018. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted for publication in A&A</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> A&A 620, A142 (2018) </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a href="https://info.arxiv.org/help">Help</a></li> </ul> </div> <div class="column"> <ul class="nav-spaced"> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>contact arXiv</title><desc>Click here to contact arXiv</desc><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 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