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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.15611">arXiv:2411.15611</a> <span> [<a href="https://arxiv.org/pdf/2411.15611">pdf</a>, <a href="https://arxiv.org/format/2411.15611">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"> Knowledge Transfer Across Modalities with Natural Language Supervision </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Barbano%2C+C+A">Carlo Alberto Barbano</a>, <a href="/search/cs?searchtype=author&query=Molinaro%2C+L">Luca Molinaro</a>, <a href="/search/cs?searchtype=author&query=Aiello%2C+E">Emanuele Aiello</a>, <a href="/search/cs?searchtype=author&query=Grangetto%2C+M">Marco Grangetto</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="2411.15611v1-abstract-short" style="display: inline;"> We present a way to learn novel concepts by only using their textual description. We call this method Knowledge Transfer. Similarly to human perception, we leverage cross-modal interaction to introduce new concepts. We hypothesize that in a pre-trained visual encoder there are enough low-level features already learned (e.g. shape, appearance, color) that can be used to describe previously unknown… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.15611v1-abstract-full').style.display = 'inline'; document.getElementById('2411.15611v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.15611v1-abstract-full" style="display: none;"> We present a way to learn novel concepts by only using their textual description. We call this method Knowledge Transfer. Similarly to human perception, we leverage cross-modal interaction to introduce new concepts. We hypothesize that in a pre-trained visual encoder there are enough low-level features already learned (e.g. shape, appearance, color) that can be used to describe previously unknown high-level concepts. Provided with a textual description of the novel concept, our method works by aligning the known low-level features of the visual encoder to its high-level textual description. We show that Knowledge Transfer can successfully introduce novel concepts in multimodal models, in a very efficient manner, by only requiring a single description of the target concept. Our approach is compatible with both separate textual and visual encoders (e.g. CLIP) and shared parameters across modalities. We also show that, following the same principle, Knowledge Transfer can improve concepts already known by the model. Leveraging Knowledge Transfer we improve zero-shot performance across different tasks such as classification, segmentation, image-text retrieval, and captioning. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.15611v1-abstract-full').style.display = 'none'; document.getElementById('2411.15611v1-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> 23 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 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">21 pages, 7 figures, 17 tables</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68T45 (Primary) 68T50 (Secondary) <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.6 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.09570">arXiv:2408.09570</a> <span> [<a href="https://arxiv.org/pdf/2408.09570">pdf</a>, <a href="https://arxiv.org/format/2408.09570">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> </div> </div> <p class="title is-5 mathjax"> Say My Name: a Model's Bias Discovery Framework </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ciranni%2C+M">Massimiliano Ciranni</a>, <a href="/search/cs?searchtype=author&query=Molinaro%2C+L">Luca Molinaro</a>, <a href="/search/cs?searchtype=author&query=Barbano%2C+C+A">Carlo Alberto Barbano</a>, <a href="/search/cs?searchtype=author&query=Fiandrotti%2C+A">Attilio Fiandrotti</a>, <a href="/search/cs?searchtype=author&query=Murino%2C+V">Vittorio Murino</a>, <a href="/search/cs?searchtype=author&query=Pastore%2C+V+P">Vito Paolo Pastore</a>, <a href="/search/cs?searchtype=author&query=Tartaglione%2C+E">Enzo Tartaglione</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="2408.09570v1-abstract-short" style="display: inline;"> In the last few years, due to the broad applicability of deep learning to downstream tasks and end-to-end training capabilities, increasingly more concerns about potential biases to specific, non-representative patterns have been raised. Many works focusing on unsupervised debiasing usually leverage the tendency of deep models to learn ``easier'' samples, for example by clustering the latent space… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.09570v1-abstract-full').style.display = 'inline'; document.getElementById('2408.09570v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.09570v1-abstract-full" style="display: none;"> In the last few years, due to the broad applicability of deep learning to downstream tasks and end-to-end training capabilities, increasingly more concerns about potential biases to specific, non-representative patterns have been raised. Many works focusing on unsupervised debiasing usually leverage the tendency of deep models to learn ``easier'' samples, for example by clustering the latent space to obtain bias pseudo-labels. However, the interpretation of such pseudo-labels is not trivial, especially for a non-expert end user, as it does not provide semantic information about the bias features. To address this issue, we introduce ``Say My Name'' (SaMyNa), the first tool to identify biases within deep models semantically. Unlike existing methods, our approach focuses on biases learned by the model. Our text-based pipeline enhances explainability and supports debiasing efforts: applicable during either training or post-hoc validation, our method can disentangle task-related information and proposes itself as a tool to analyze biases. Evaluation on traditional benchmarks demonstrates its effectiveness in detecting biases and even disclaiming them, showcasing its broad applicability for model diagnosis. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.09570v1-abstract-full').style.display = 'none'; document.getElementById('2408.09570v1-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> 18 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/2408.07079">arXiv:2408.07079</a> <span> [<a href="https://arxiv.org/pdf/2408.07079">pdf</a>, <a href="https://arxiv.org/format/2408.07079">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="Artificial Intelligence">cs.AI</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"> Anatomical Foundation Models for Brain MRIs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Barbano%2C+C+A">Carlo Alberto Barbano</a>, <a href="/search/cs?searchtype=author&query=Brunello%2C+M">Matteo Brunello</a>, <a href="/search/cs?searchtype=author&query=Dufumier%2C+B">Benoit Dufumier</a>, <a href="/search/cs?searchtype=author&query=Grangetto%2C+M">Marco Grangetto</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="2408.07079v2-abstract-short" style="display: inline;"> Deep Learning (DL) in neuroimaging has become increasingly relevant for detecting neurological conditions and neurodegenerative disorders. One of the most predominant biomarkers in neuroimaging is represented by brain age, which has been shown to be a good indicator for different conditions, such as Alzheimer's Disease. Using brain age for pretraining DL models in transfer learning settings has al… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.07079v2-abstract-full').style.display = 'inline'; document.getElementById('2408.07079v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.07079v2-abstract-full" style="display: none;"> Deep Learning (DL) in neuroimaging has become increasingly relevant for detecting neurological conditions and neurodegenerative disorders. One of the most predominant biomarkers in neuroimaging is represented by brain age, which has been shown to be a good indicator for different conditions, such as Alzheimer's Disease. Using brain age for pretraining DL models in transfer learning settings has also recently shown promising results, especially when dealing with data scarcity of different conditions. On the other hand, anatomical information of brain MRIs (e.g. cortical thickness) can provide important information for learning good representations that can be transferred to many downstream tasks. In this work, we propose AnatCL, an anatomical foundation model for brain MRIs that i.) leverages anatomical information with a weakly contrastive learning approach and ii.) achieves state-of-the-art performances in many different downstream tasks. To validate our approach we consider 12 different downstream tasks for diagnosis classification, and prediction of 10 different clinical assessment scores. Pretrained models can be found at https://github.com/EIDOSLAB/AnatCL. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.07079v2-abstract-full').style.display = 'none'; document.getElementById('2408.07079v2-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> 5 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 7 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 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">12 pages; added source url</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68T07 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.6 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.02077">arXiv:2406.02077</a> <span> [<a href="https://arxiv.org/pdf/2406.02077">pdf</a>, <a href="https://arxiv.org/format/2406.02077">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="Artificial Intelligence">cs.AI</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"> Multi-target stain normalization for histology slides </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ivanov%2C+D">Desislav Ivanov</a>, <a href="/search/cs?searchtype=author&query=Barbano%2C+C+A">Carlo Alberto Barbano</a>, <a href="/search/cs?searchtype=author&query=Grangetto%2C+M">Marco Grangetto</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="2406.02077v3-abstract-short" style="display: inline;"> Traditional staining normalization approaches, e.g. Macenko, typically rely on the choice of a single representative reference image, which may not adequately account for the diverse staining patterns of datasets collected in practical scenarios. In this study, we introduce a novel approach that leverages multiple reference images to enhance robustness against stain variation. Our method is parame… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.02077v3-abstract-full').style.display = 'inline'; document.getElementById('2406.02077v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.02077v3-abstract-full" style="display: none;"> Traditional staining normalization approaches, e.g. Macenko, typically rely on the choice of a single representative reference image, which may not adequately account for the diverse staining patterns of datasets collected in practical scenarios. In this study, we introduce a novel approach that leverages multiple reference images to enhance robustness against stain variation. Our method is parameter-free and can be adopted in existing computational pathology pipelines with no significant changes. We evaluate the effectiveness of our method through experiments using a deep-learning pipeline for automatic nuclei segmentation on colorectal images. Our results show that by leveraging multiple reference images, better results can be achieved when generalizing to external data, where the staining can widely differ from the training set. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.02077v3-abstract-full').style.display = 'none'; document.getElementById('2406.02077v3-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 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68U10 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.4.0 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.00772">arXiv:2406.00772</a> <span> [<a href="https://arxiv.org/pdf/2406.00772">pdf</a>, <a href="https://arxiv.org/format/2406.00772">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"> Unsupervised Contrastive Analysis for Salient Pattern Detection using Conditional Diffusion Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Patr%C3%ADcio%2C+C">Cristiano Patr铆cio</a>, <a href="/search/cs?searchtype=author&query=Barbano%2C+C+A">Carlo Alberto Barbano</a>, <a href="/search/cs?searchtype=author&query=Fiandrotti%2C+A">Attilio Fiandrotti</a>, <a href="/search/cs?searchtype=author&query=Renzulli%2C+R">Riccardo Renzulli</a>, <a href="/search/cs?searchtype=author&query=Grangetto%2C+M">Marco Grangetto</a>, <a href="/search/cs?searchtype=author&query=Teixeira%2C+L+F">Luis F. Teixeira</a>, <a href="/search/cs?searchtype=author&query=Neves%2C+J+C">Jo茫o C. Neves</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="2406.00772v2-abstract-short" style="display: inline;"> Contrastive Analysis (CA) regards the problem of identifying patterns in images that allow distinguishing between a background (BG) dataset (i.e. healthy subjects) and a target (TG) dataset (i.e. unhealthy subjects). Recent works on this topic rely on variational autoencoders (VAE) or contrastive learning strategies to learn the patterns that separate TG samples from BG samples in a supervised man… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.00772v2-abstract-full').style.display = 'inline'; document.getElementById('2406.00772v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.00772v2-abstract-full" style="display: none;"> Contrastive Analysis (CA) regards the problem of identifying patterns in images that allow distinguishing between a background (BG) dataset (i.e. healthy subjects) and a target (TG) dataset (i.e. unhealthy subjects). Recent works on this topic rely on variational autoencoders (VAE) or contrastive learning strategies to learn the patterns that separate TG samples from BG samples in a supervised manner. However, the dependency on target (unhealthy) samples can be challenging in medical scenarios due to their limited availability. Also, the blurred reconstructions of VAEs lack utility and interpretability. In this work, we redefine the CA task by employing a self-supervised contrastive encoder to learn a latent representation encoding only common patterns from input images, using samples exclusively from the BG dataset during training, and approximating the distribution of the target patterns by leveraging data augmentation techniques. Subsequently, we exploit state-of-the-art generative methods, i.e. diffusion models, conditioned on the learned latent representation to produce a realistic (healthy) version of the input image encoding solely the common patterns. Thorough validation on a facial image dataset and experiments across three brain MRI datasets demonstrate that conditioning the generative process of state-of-the-art generative methods with the latent representation from our self-supervised contrastive encoder yields improvements in the generated image quality and in the accuracy of image classification. The code is available at https://github.com/CristianoPatricio/unsupervised-contrastive-cond-diff. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.00772v2-abstract-full').style.display = 'none'; document.getElementById('2406.00772v2-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 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 2 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 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">18 pages, 11 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/2405.11598">arXiv:2405.11598</a> <span> [<a href="https://arxiv.org/pdf/2405.11598">pdf</a>, <a href="https://arxiv.org/format/2405.11598">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="Artificial Intelligence">cs.AI</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"> AI-Assisted Diagnosis for Covid-19 CXR Screening: From Data Collection to Clinical Validation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Barbano%2C+C+A">Carlo Alberto Barbano</a>, <a href="/search/cs?searchtype=author&query=Renzulli%2C+R">Riccardo Renzulli</a>, <a href="/search/cs?searchtype=author&query=Grosso%2C+M">Marco Grosso</a>, <a href="/search/cs?searchtype=author&query=Basile%2C+D">Domenico Basile</a>, <a href="/search/cs?searchtype=author&query=Busso%2C+M">Marco Busso</a>, <a href="/search/cs?searchtype=author&query=Grangetto%2C+M">Marco Grangetto</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="2405.11598v1-abstract-short" style="display: inline;"> In this paper, we present the major results from the Covid Radiographic imaging System based on AI (Co.R.S.A.) project, which took place in Italy. This project aims to develop a state-of-the-art AI-based system for diagnosing Covid-19 pneumonia from Chest X-ray (CXR) images. The contributions of this work are manyfold: the release of the public CORDA dataset, a deep learning pipeline for Covid-19… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.11598v1-abstract-full').style.display = 'inline'; document.getElementById('2405.11598v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.11598v1-abstract-full" style="display: none;"> In this paper, we present the major results from the Covid Radiographic imaging System based on AI (Co.R.S.A.) project, which took place in Italy. This project aims to develop a state-of-the-art AI-based system for diagnosing Covid-19 pneumonia from Chest X-ray (CXR) images. The contributions of this work are manyfold: the release of the public CORDA dataset, a deep learning pipeline for Covid-19 detection, and the clinical validation of the developed solution by expert radiologists. The proposed detection model is based on a two-step approach that, paired with state-of-the-art debiasing, provides reliable results. Most importantly, our investigation includes the actual usage of the diagnosis aid tool by radiologists, allowing us to assess the real benefits in terms of accuracy and time efficiency. Project homepage: https://corsa.di.unito.it/ <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.11598v1-abstract-full').style.display = 'none'; document.getElementById('2405.11598v1-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 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">Accepted at 21st IEEE International Symposium on Biomedical Imaging (ISBI)</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68T07 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.1; I.4.0 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.18756">arXiv:2403.18756</a> <span> [<a href="https://arxiv.org/pdf/2403.18756">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> <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"> Detection of subclinical atherosclerosis by image-based deep learning on chest x-ray </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gallone%2C+G">Guglielmo Gallone</a>, <a href="/search/cs?searchtype=author&query=Iodice%2C+F">Francesco Iodice</a>, <a href="/search/cs?searchtype=author&query=Presta%2C+A">Alberto Presta</a>, <a href="/search/cs?searchtype=author&query=Tore%2C+D">Davide Tore</a>, <a href="/search/cs?searchtype=author&query=de+Filippo%2C+O">Ovidio de Filippo</a>, <a href="/search/cs?searchtype=author&query=Visciano%2C+M">Michele Visciano</a>, <a href="/search/cs?searchtype=author&query=Barbano%2C+C+A">Carlo Alberto Barbano</a>, <a href="/search/cs?searchtype=author&query=Serafini%2C+A">Alessandro Serafini</a>, <a href="/search/cs?searchtype=author&query=Gorrini%2C+P">Paola Gorrini</a>, <a href="/search/cs?searchtype=author&query=Bruno%2C+A">Alessandro Bruno</a>, <a href="/search/cs?searchtype=author&query=Marra%2C+W+G">Walter Grosso Marra</a>, <a href="/search/cs?searchtype=author&query=Hughes%2C+J">James Hughes</a>, <a href="/search/cs?searchtype=author&query=Iannaccone%2C+M">Mario Iannaccone</a>, <a href="/search/cs?searchtype=author&query=Fonio%2C+P">Paolo Fonio</a>, <a href="/search/cs?searchtype=author&query=Fiandrotti%2C+A">Attilio Fiandrotti</a>, <a href="/search/cs?searchtype=author&query=Depaoli%2C+A">Alessandro Depaoli</a>, <a href="/search/cs?searchtype=author&query=Grangetto%2C+M">Marco Grangetto</a>, <a href="/search/cs?searchtype=author&query=de+Ferrari%2C+G+M">Gaetano Maria de Ferrari</a>, <a href="/search/cs?searchtype=author&query=D%27Ascenzo%2C+F">Fabrizio D'Ascenzo</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="2403.18756v1-abstract-short" style="display: inline;"> Aims. To develop a deep-learning based system for recognition of subclinical atherosclerosis on a plain frontal chest x-ray. Methods and Results. A deep-learning algorithm to predict coronary artery calcium (CAC) score (the AI-CAC model) was developed on 460 chest x-ray (80% training cohort, 20% internal validation cohort) of primary prevention patients (58.4% male, median age 63 [51-74] years) wi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.18756v1-abstract-full').style.display = 'inline'; document.getElementById('2403.18756v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.18756v1-abstract-full" style="display: none;"> Aims. To develop a deep-learning based system for recognition of subclinical atherosclerosis on a plain frontal chest x-ray. Methods and Results. A deep-learning algorithm to predict coronary artery calcium (CAC) score (the AI-CAC model) was developed on 460 chest x-ray (80% training cohort, 20% internal validation cohort) of primary prevention patients (58.4% male, median age 63 [51-74] years) with available paired chest x-ray and chest computed tomography (CT) indicated for any clinical reason and performed within 3 months. The CAC score calculated on chest CT was used as ground truth. The model was validated on an temporally-independent cohort of 90 patients from the same institution (external validation). The diagnostic accuracy of the AI-CAC model assessed by the area under the curve (AUC) was the primary outcome. Overall, median AI-CAC score was 35 (0-388) and 28.9% patients had no AI-CAC. AUC of the AI-CAC model to identify a CAC>0 was 0.90 in the internal validation cohort and 0.77 in the external validation cohort. Sensitivity was consistently above 92% in both cohorts. In the overall cohort (n=540), among patients with AI-CAC=0, a single ASCVD event occurred, after 4.3 years. Patients with AI-CAC>0 had significantly higher Kaplan Meier estimates for ASCVD events (13.5% vs. 3.4%, log-rank=0.013). Conclusion. The AI-CAC model seems to accurately detect subclinical atherosclerosis on chest x-ray with elevated sensitivity, and to predict ASCVD events with elevated negative predictive value. Adoption of the AI-CAC model to refine CV risk stratification or as an opportunistic screening tool requires prospective evaluation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.18756v1-abstract-full').style.display = 'none'; document.getElementById('2403.18756v1-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> 27 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 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">Submitted to European Heart Journal - Cardiovascular Imaging Added also the additional material 44 pages (30 main paper, 14 additional material), 14 figures (5 main manuscript, 9 additional material)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2211.08326">arXiv:2211.08326</a> <span> [<a href="https://arxiv.org/pdf/2211.08326">pdf</a>, <a href="https://arxiv.org/format/2211.08326">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"> Contrastive learning for regression in multi-site brain age prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Barbano%2C+C+A">Carlo Alberto Barbano</a>, <a href="/search/cs?searchtype=author&query=Dufumier%2C+B">Benoit Dufumier</a>, <a href="/search/cs?searchtype=author&query=Duchesnay%2C+E">Edouard Duchesnay</a>, <a href="/search/cs?searchtype=author&query=Grangetto%2C+M">Marco Grangetto</a>, <a href="/search/cs?searchtype=author&query=Gori%2C+P">Pietro Gori</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="2211.08326v2-abstract-short" style="display: inline;"> Building accurate Deep Learning (DL) models for brain age prediction is a very relevant topic in neuroimaging, as it could help better understand neurodegenerative disorders and find new biomarkers. To estimate accurate and generalizable models, large datasets have been collected, which are often multi-site and multi-scanner. This large heterogeneity negatively affects the generalization performan… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.08326v2-abstract-full').style.display = 'inline'; document.getElementById('2211.08326v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2211.08326v2-abstract-full" style="display: none;"> Building accurate Deep Learning (DL) models for brain age prediction is a very relevant topic in neuroimaging, as it could help better understand neurodegenerative disorders and find new biomarkers. To estimate accurate and generalizable models, large datasets have been collected, which are often multi-site and multi-scanner. This large heterogeneity negatively affects the generalization performance of DL models since they are prone to overfit site-related noise. Recently, contrastive learning approaches have been shown to be more robust against noise in data or labels. For this reason, we propose a novel contrastive learning regression loss for robust brain age prediction using MRI scans. Our method achieves state-of-the-art performance on the OpenBHB challenge, yielding the best generalization capability and robustness to site-related noise. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.08326v2-abstract-full').style.display = 'none'; document.getElementById('2211.08326v2-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 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 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">5 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/2211.05568">arXiv:2211.05568</a> <span> [<a href="https://arxiv.org/pdf/2211.05568">pdf</a>, <a href="https://arxiv.org/format/2211.05568">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</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">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Unbiased Supervised Contrastive Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Barbano%2C+C+A">Carlo Alberto Barbano</a>, <a href="/search/cs?searchtype=author&query=Dufumier%2C+B">Benoit Dufumier</a>, <a href="/search/cs?searchtype=author&query=Tartaglione%2C+E">Enzo Tartaglione</a>, <a href="/search/cs?searchtype=author&query=Grangetto%2C+M">Marco Grangetto</a>, <a href="/search/cs?searchtype=author&query=Gori%2C+P">Pietro Gori</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="2211.05568v4-abstract-short" style="display: inline;"> Many datasets are biased, namely they contain easy-to-learn features that are highly correlated with the target class only in the dataset but not in the true underlying distribution of the data. For this reason, learning unbiased models from biased data has become a very relevant research topic in the last years. In this work, we tackle the problem of learning representations that are robust to bi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.05568v4-abstract-full').style.display = 'inline'; document.getElementById('2211.05568v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2211.05568v4-abstract-full" style="display: none;"> Many datasets are biased, namely they contain easy-to-learn features that are highly correlated with the target class only in the dataset but not in the true underlying distribution of the data. For this reason, learning unbiased models from biased data has become a very relevant research topic in the last years. In this work, we tackle the problem of learning representations that are robust to biases. We first present a margin-based theoretical framework that allows us to clarify why recent contrastive losses (InfoNCE, SupCon, etc.) can fail when dealing with biased data. Based on that, we derive a novel formulation of the supervised contrastive loss (epsilon-SupInfoNCE), providing more accurate control of the minimal distance between positive and negative samples. Furthermore, thanks to our theoretical framework, we also propose FairKL, a new debiasing regularization loss, that works well even with extremely biased data. We validate the proposed losses on standard vision datasets including CIFAR10, CIFAR100, and ImageNet, and we assess the debiasing capability of FairKL with epsilon-SupInfoNCE, reaching state-of-the-art performance on a number of biased datasets, including real instances of biases in the wild. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.05568v4-abstract-full').style.display = 'none'; document.getElementById('2211.05568v4-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 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 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">Accepted at ICLR 2023 (v3); Fix typo in Eq.19 (v4)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2206.01646">arXiv:2206.01646</a> <span> [<a href="https://arxiv.org/pdf/2206.01646">pdf</a>, <a href="https://arxiv.org/format/2206.01646">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"> Integrating Prior Knowledge in Contrastive Learning with Kernel </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Dufumier%2C+B">Benoit Dufumier</a>, <a href="/search/cs?searchtype=author&query=Barbano%2C+C+A">Carlo Alberto Barbano</a>, <a href="/search/cs?searchtype=author&query=Louiset%2C+R">Robin Louiset</a>, <a href="/search/cs?searchtype=author&query=Duchesnay%2C+E">Edouard Duchesnay</a>, <a href="/search/cs?searchtype=author&query=Gori%2C+P">Pietro Gori</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="2206.01646v2-abstract-short" style="display: inline;"> Data augmentation is a crucial component in unsupervised contrastive learning (CL). It determines how positive samples are defined and, ultimately, the quality of the learned representation. In this work, we open the door to new perspectives for CL by integrating prior knowledge, given either by generative models -- viewed as prior representations -- or weak attributes in the positive and negative… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.01646v2-abstract-full').style.display = 'inline'; document.getElementById('2206.01646v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2206.01646v2-abstract-full" style="display: none;"> Data augmentation is a crucial component in unsupervised contrastive learning (CL). It determines how positive samples are defined and, ultimately, the quality of the learned representation. In this work, we open the door to new perspectives for CL by integrating prior knowledge, given either by generative models -- viewed as prior representations -- or weak attributes in the positive and negative sampling. To this end, we use kernel theory to propose a novel loss, called decoupled uniformity, that i) allows the integration of prior knowledge and ii) removes the negative-positive coupling in the original InfoNCE loss. We draw a connection between contrastive learning and conditional mean embedding theory to derive tight bounds on the downstream classification loss. In an unsupervised setting, we empirically demonstrate that CL benefits from generative models to improve its representation both on natural and medical images. In a weakly supervised scenario, our framework outperforms other unconditional and conditional CL approaches. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.01646v2-abstract-full').style.display = 'none'; document.getElementById('2206.01646v2-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> 30 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 3 June, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 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">ICML 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/2204.12941">arXiv:2204.12941</a> <span> [<a href="https://arxiv.org/pdf/2204.12941">pdf</a>, <a href="https://arxiv.org/format/2204.12941">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</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"> Unsupervised Learning of Unbiased Visual Representations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Barbano%2C+C+A">Carlo Alberto Barbano</a>, <a href="/search/cs?searchtype=author&query=Tartaglione%2C+E">Enzo Tartaglione</a>, <a href="/search/cs?searchtype=author&query=Grangetto%2C+M">Marco Grangetto</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="2204.12941v1-abstract-short" style="display: inline;"> Deep neural networks are known for their inability to learn robust representations when biases exist in the dataset. This results in a poor generalization to unbiased datasets, as the predictions strongly rely on peripheral and confounding factors, which are erroneously learned by the network. Many existing works deal with this issue by either employing an explicit supervision on the bias attribut… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.12941v1-abstract-full').style.display = 'inline'; document.getElementById('2204.12941v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2204.12941v1-abstract-full" style="display: none;"> Deep neural networks are known for their inability to learn robust representations when biases exist in the dataset. This results in a poor generalization to unbiased datasets, as the predictions strongly rely on peripheral and confounding factors, which are erroneously learned by the network. Many existing works deal with this issue by either employing an explicit supervision on the bias attributes, or assuming prior knowledge about the bias. In this work we study this problem in a more difficult scenario, in which no explicit annotation about the bias is available, and without any prior knowledge about its nature. We propose a fully unsupervised debiasing framework, consisting of three steps: first, we exploit the natural preference for learning malignant biases, obtaining a bias-capturing model; then, we perform a pseudo-labelling step to obtain bias labels; finally we employ state-of-the-art supervised debiasing techniques to obtain an unbiased model. We also propose a theoretical framework to assess the biasness of a model, and provide a detailed analysis on how biases affect the training of neural networks. We perform experiments on synthetic and real-world datasets, showing that our method achieves state-of-the-art performance in a variety of settings, sometimes even higher than fully supervised debiasing approaches. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.12941v1-abstract-full').style.display = 'none'; document.getElementById('2204.12941v1-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> 26 April, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 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">14 pages, 8 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68T07 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2103.02023">arXiv:2103.02023</a> <span> [<a href="https://arxiv.org/pdf/2103.02023">pdf</a>, <a href="https://arxiv.org/format/2103.02023">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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</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/10.1109/CVPR46437.2021.01330">10.1109/CVPR46437.2021.01330 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> EnD: Entangling and Disentangling deep representations for bias correction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Tartaglione%2C+E">Enzo Tartaglione</a>, <a href="/search/cs?searchtype=author&query=Barbano%2C+C+A">Carlo Alberto Barbano</a>, <a href="/search/cs?searchtype=author&query=Grangetto%2C+M">Marco Grangetto</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="2103.02023v1-abstract-short" style="display: inline;"> Artificial neural networks perform state-of-the-art in an ever-growing number of tasks, and nowadays they are used to solve an incredibly large variety of tasks. There are problems, like the presence of biases in the training data, which question the generalization capability of these models. In this work we propose EnD, a regularization strategy whose aim is to prevent deep models from learning u… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.02023v1-abstract-full').style.display = 'inline'; document.getElementById('2103.02023v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2103.02023v1-abstract-full" style="display: none;"> Artificial neural networks perform state-of-the-art in an ever-growing number of tasks, and nowadays they are used to solve an incredibly large variety of tasks. There are problems, like the presence of biases in the training data, which question the generalization capability of these models. In this work we propose EnD, a regularization strategy whose aim is to prevent deep models from learning unwanted biases. In particular, we insert an "information bottleneck" at a certain point of the deep neural network, where we disentangle the information about the bias, still letting the useful information for the training task forward-propagating in the rest of the model. One big advantage of EnD is that we do not require additional training complexity (like decoders or extra layers in the model), since it is a regularizer directly applied on the trained model. Our experiments show that EnD effectively improves the generalization on unbiased test sets, and it can be effectively applied on real-case scenarios, like removing hidden biases in the COVID-19 detection from radiographic images. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.02023v1-abstract-full').style.display = 'none'; document.getElementById('2103.02023v1-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 March, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2101.11589">arXiv:2101.11589</a> <span> [<a href="https://arxiv.org/pdf/2101.11589">pdf</a>, <a href="https://arxiv.org/format/2101.11589">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</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/10.1088/1748-0221/16/07/P07041">10.1088/1748-0221/16/07/P07041 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> A Convolutional Neural Network based Cascade Reconstruction for the IceCube Neutrino Observatory </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Abbasi%2C+R">R. Abbasi</a>, <a href="/search/cs?searchtype=author&query=Ackermann%2C+M">M. Ackermann</a>, <a href="/search/cs?searchtype=author&query=Adams%2C+J">J. Adams</a>, <a href="/search/cs?searchtype=author&query=Aguilar%2C+J+A">J. A. Aguilar</a>, <a href="/search/cs?searchtype=author&query=Ahlers%2C+M">M. Ahlers</a>, <a href="/search/cs?searchtype=author&query=Ahrens%2C+M">M. Ahrens</a>, <a href="/search/cs?searchtype=author&query=Alispach%2C+C">C. Alispach</a>, <a href="/search/cs?searchtype=author&query=Alves%2C+A+A">A. A. Alves Jr.</a>, <a href="/search/cs?searchtype=author&query=Amin%2C+N+M">N. M. Amin</a>, <a href="/search/cs?searchtype=author&query=An%2C+R">R. An</a>, <a href="/search/cs?searchtype=author&query=Andeen%2C+K">K. Andeen</a>, <a href="/search/cs?searchtype=author&query=Anderson%2C+T">T. Anderson</a>, <a href="/search/cs?searchtype=author&query=Ansseau%2C+I">I. Ansseau</a>, <a href="/search/cs?searchtype=author&query=Anton%2C+G">G. Anton</a>, <a href="/search/cs?searchtype=author&query=Arg%C3%BCelles%2C+C">C. Arg眉elles</a>, <a href="/search/cs?searchtype=author&query=Axani%2C+S">S. Axani</a>, <a href="/search/cs?searchtype=author&query=Bai%2C+X">X. Bai</a>, <a href="/search/cs?searchtype=author&query=V.%2C+A+B">A. Balagopal V.</a>, <a href="/search/cs?searchtype=author&query=Barbano%2C+A">A. Barbano</a>, <a href="/search/cs?searchtype=author&query=Barwick%2C+S+W">S. W. Barwick</a>, <a href="/search/cs?searchtype=author&query=Bastian%2C+B">B. Bastian</a>, <a href="/search/cs?searchtype=author&query=Basu%2C+V">V. Basu</a>, <a href="/search/cs?searchtype=author&query=Baum%2C+V">V. Baum</a>, <a href="/search/cs?searchtype=author&query=Baur%2C+S">S. Baur</a>, <a href="/search/cs?searchtype=author&query=Bay%2C+R">R. Bay</a> , et al. (343 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="2101.11589v2-abstract-short" style="display: inline;"> Continued improvements on existing reconstruction methods are vital to the success of high-energy physics experiments, such as the IceCube Neutrino Observatory. In IceCube, further challenges arise as the detector is situated at the geographic South Pole where computational resources are limited. However, to perform real-time analyses and to issue alerts to telescopes around the world, powerful an… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.11589v2-abstract-full').style.display = 'inline'; document.getElementById('2101.11589v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2101.11589v2-abstract-full" style="display: none;"> Continued improvements on existing reconstruction methods are vital to the success of high-energy physics experiments, such as the IceCube Neutrino Observatory. In IceCube, further challenges arise as the detector is situated at the geographic South Pole where computational resources are limited. However, to perform real-time analyses and to issue alerts to telescopes around the world, powerful and fast reconstruction methods are desired. Deep neural networks can be extremely powerful, and their usage is computationally inexpensive once the networks are trained. These characteristics make a deep learning-based approach an excellent candidate for the application in IceCube. A reconstruction method based on convolutional architectures and hexagonally shaped kernels is presented. The presented method is robust towards systematic uncertainties in the simulation and has been tested on experimental data. In comparison to standard reconstruction methods in IceCube, it can improve upon the reconstruction accuracy, while reducing the time necessary to run the reconstruction by two to three orders of magnitude. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.11589v2-abstract-full').style.display = 'none'; document.getElementById('2101.11589v2-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> 26 July, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 27 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">39 pages, 15 figures, submitted to Journal of Instrumentation; added references</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> JINST 16 (2021) P07041 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2101.10223">arXiv:2101.10223</a> <span> [<a href="https://arxiv.org/pdf/2101.10223">pdf</a>, <a href="https://arxiv.org/format/2101.10223">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 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/10.1007/978-3-031-06427-2_15">10.1007/978-3-031-06427-2_15 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> A two-step explainable approach for COVID-19 computer-aided diagnosis from chest x-ray images </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Barbano%2C+C+A">Carlo Alberto Barbano</a>, <a href="/search/cs?searchtype=author&query=Tartaglione%2C+E">Enzo Tartaglione</a>, <a href="/search/cs?searchtype=author&query=Berzovini%2C+C">Claudio Berzovini</a>, <a href="/search/cs?searchtype=author&query=Calandri%2C+M">Marco Calandri</a>, <a href="/search/cs?searchtype=author&query=Grangetto%2C+M">Marco Grangetto</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="2101.10223v1-abstract-short" style="display: inline;"> Early screening of patients is a critical issue in order to assess immediate and fast responses against the spread of COVID-19. The use of nasopharyngeal swabs has been considered the most viable approach; however, the result is not immediate or, in the case of fast exams, sufficiently accurate. Using Chest X-Ray (CXR) imaging for early screening potentially provides faster and more accurate respo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.10223v1-abstract-full').style.display = 'inline'; document.getElementById('2101.10223v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2101.10223v1-abstract-full" style="display: none;"> Early screening of patients is a critical issue in order to assess immediate and fast responses against the spread of COVID-19. The use of nasopharyngeal swabs has been considered the most viable approach; however, the result is not immediate or, in the case of fast exams, sufficiently accurate. Using Chest X-Ray (CXR) imaging for early screening potentially provides faster and more accurate response; however, diagnosing COVID from CXRs is hard and we should rely on deep learning support, whose decision process is, on the other hand, "black-boxed" and, for such reason, untrustworthy. We propose an explainable two-step diagnostic approach, where we first detect known pathologies (anomalies) in the lungs, on top of which we diagnose the illness. Our approach achieves promising performance in COVID detection, compatible with expert human radiologists. All of our experiments have been carried out bearing in mind that, especially for clinical applications, explainability plays a major role for building trust in machine learning algorithms. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.10223v1-abstract-full').style.display = 'none'; document.getElementById('2101.10223v1-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> 25 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">5 pages, 4 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.0; I.2.6 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2101.09991">arXiv:2101.09991</a> <span> [<a href="https://arxiv.org/pdf/2101.09991">pdf</a>, <a href="https://arxiv.org/format/2101.09991">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 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/10.1109/ICIP42928.2021.9506198">10.1109/ICIP42928.2021.9506198 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> UniToPatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Barbano%2C+C+A">Carlo Alberto Barbano</a>, <a href="/search/cs?searchtype=author&query=Perlo%2C+D">Daniele Perlo</a>, <a href="/search/cs?searchtype=author&query=Tartaglione%2C+E">Enzo Tartaglione</a>, <a href="/search/cs?searchtype=author&query=Fiandrotti%2C+A">Attilio Fiandrotti</a>, <a href="/search/cs?searchtype=author&query=Bertero%2C+L">Luca Bertero</a>, <a href="/search/cs?searchtype=author&query=Cassoni%2C+P">Paola Cassoni</a>, <a href="/search/cs?searchtype=author&query=Grangetto%2C+M">Marco Grangetto</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="2101.09991v2-abstract-short" style="display: inline;"> Histopathological characterization of colorectal polyps allows to tailor patients' management and follow up with the ultimate aim of avoiding or promptly detecting an invasive carcinoma. Colorectal polyps characterization relies on the histological analysis of tissue samples to determine the polyps malignancy and dysplasia grade. Deep neural networks achieve outstanding accuracy in medical pattern… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.09991v2-abstract-full').style.display = 'inline'; document.getElementById('2101.09991v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2101.09991v2-abstract-full" style="display: none;"> Histopathological characterization of colorectal polyps allows to tailor patients' management and follow up with the ultimate aim of avoiding or promptly detecting an invasive carcinoma. Colorectal polyps characterization relies on the histological analysis of tissue samples to determine the polyps malignancy and dysplasia grade. Deep neural networks achieve outstanding accuracy in medical patterns recognition, however they require large sets of annotated training images. We introduce UniToPatho, an annotated dataset of 9536 hematoxylin and eosin (H&E) stained patches extracted from 292 whole-slide images, meant for training deep neural networks for colorectal polyps classification and adenomas grading. We present our dataset and provide insights on how to tackle the problem of automatic colorectal polyps characterization. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.09991v2-abstract-full').style.display = 'none'; document.getElementById('2101.09991v2-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 February, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 25 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">5 pages, 3 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.0; I.2.6 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2004.05405">arXiv:2004.05405</a> <span> [<a href="https://arxiv.org/pdf/2004.05405">pdf</a>, <a href="https://arxiv.org/format/2004.05405">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 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/10.3390/ijerph17186933">10.3390/ijerph17186933 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Tartaglione%2C+E">Enzo Tartaglione</a>, <a href="/search/cs?searchtype=author&query=Barbano%2C+C+A">Carlo Alberto Barbano</a>, <a href="/search/cs?searchtype=author&query=Berzovini%2C+C">Claudio Berzovini</a>, <a href="/search/cs?searchtype=author&query=Calandri%2C+M">Marco Calandri</a>, <a href="/search/cs?searchtype=author&query=Grangetto%2C+M">Marco Grangetto</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="2004.05405v1-abstract-short" style="display: inline;"> The possibility to use widespread and simple chest X-ray (CXR) imaging for early screening of COVID-19 patients is attracting much interest from both the clinical and the AI community. In this study we provide insights and also raise warnings on what is reasonable to expect by applying deep-learning to COVID classification of CXR images. We provide a methodological guide and critical reading of an… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.05405v1-abstract-full').style.display = 'inline'; document.getElementById('2004.05405v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2004.05405v1-abstract-full" style="display: none;"> The possibility to use widespread and simple chest X-ray (CXR) imaging for early screening of COVID-19 patients is attracting much interest from both the clinical and the AI community. In this study we provide insights and also raise warnings on what is reasonable to expect by applying deep-learning to COVID classification of CXR images. We provide a methodological guide and critical reading of an extensive set of statistical results that can be obtained using currently available datasets. In particular, we take the challenge posed by current small size COVID data and show how significant can be the bias introduced by transfer-learning using larger public non-COVID CXR datasets. We also contribute by providing results on a medium size COVID CXR dataset, just collected by one of the major emergency hospitals in Northern Italy during the peak of the COVID pandemic. These novel data allow us to contribute to validate the generalization capacity of preliminary results circulating in the scientific community. Our conclusions shed some light into the possibility to effectively discriminate COVID using CXR. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.05405v1-abstract-full').style.display = 'none'; document.getElementById('2004.05405v1-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 April, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Int. J. Environ. Res. Public Health 2020, 17(18), 6933 </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 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg> <a href="https://info.arxiv.org/help/contact.html"> Contact</a> </li> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>subscribe to arXiv mailings</title><desc>Click here to subscribe</desc><path d="M476 3.2L12.5 270.6c-18.1 10.4-15.8 35.6 2.2 43.2L121 358.4l287.3-253.2c5.5-4.9 13.3 2.6 8.6 8.3L176 407v80.5c0 23.6 28.5 32.9 42.5 15.8L282 426l124.6 52.2c14.2 6 30.4-2.9 33-18.2l72-432C515 7.8 493.3-6.8 476 3.2z"/></svg> <a href="https://info.arxiv.org/help/subscribe"> Subscribe</a> </li> </ul> </div> </div> </div>   <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/help/license/index.html">Copyright</a></li> <li><a href="https://info.arxiv.org/help/policies/privacy_policy.html">Privacy Policy</a></li> </ul> </div> <div class="column sorry-app-links"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/help/web_accessibility.html">Web Accessibility Assistance</a></li> <li> <p class="help"> <a class="a11y-main-link" href="https://status.arxiv.org" target="_blank">arXiv Operational Status <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 256 512" class="icon filter-dark_grey" role="presentation"><path d="M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z"/></svg></a><br> Get status notifications via <a class="is-link" href="https://subscribe.sorryapp.com/24846f03/email/new" target="_blank"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><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 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg>email</a> or <a class="is-link" href="https://subscribe.sorryapp.com/24846f03/slack/new" target="_blank"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512" class="icon filter-black" role="presentation"><path d="M94.12 315.1c0 25.9-21.16 47.06-47.06 47.06S0 341 0 315.1c0-25.9 21.16-47.06 47.06-47.06h47.06v47.06zm23.72 0c0-25.9 21.16-47.06 47.06-47.06s47.06 21.16 47.06 47.06v117.84c0 25.9-21.16 47.06-47.06 47.06s-47.06-21.16-47.06-47.06V315.1zm47.06-188.98c-25.9 0-47.06-21.16-47.06-47.06S139 32 164.9 32s47.06 21.16 47.06 47.06v47.06H164.9zm0 23.72c25.9 0 47.06 21.16 47.06 47.06s-21.16 47.06-47.06 47.06H47.06C21.16 243.96 0 222.8 0 196.9s21.16-47.06 47.06-47.06H164.9zm188.98 47.06c0-25.9 21.16-47.06 47.06-47.06 25.9 0 47.06 21.16 47.06 47.06s-21.16 47.06-47.06 47.06h-47.06V196.9zm-23.72 0c0 25.9-21.16 47.06-47.06 47.06-25.9 0-47.06-21.16-47.06-47.06V79.06c0-25.9 21.16-47.06 47.06-47.06 25.9 0 47.06 21.16 47.06 47.06V196.9zM283.1 385.88c25.9 0 47.06 21.16 47.06 47.06 0 25.9-21.16 47.06-47.06 47.06-25.9 0-47.06-21.16-47.06-47.06v-47.06h47.06zm0-23.72c-25.9 0-47.06-21.16-47.06-47.06 0-25.9 21.16-47.06 47.06-47.06h117.84c25.9 0 47.06 21.16 47.06 47.06 0 25.9-21.16 47.06-47.06 47.06H283.1z"/></svg>slack</a> </p> </li> </ul> </div> </div> </div>  </div> </footer> <script src="https://static.arxiv.org/static/base/1.0.0a5/js/member_acknowledgement.js"></script> </body> </html>

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