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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/2408.07532">arXiv:2408.07532</a> <span> [<a href="https://arxiv.org/pdf/2408.07532">pdf</a>, <a href="https://arxiv.org/format/2408.07532">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"> Improved 3D Whole Heart Geometry from Sparse CMR Slices </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Xu%2C+Y">Yiyang Xu</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+H">Hao Xu</a>, <a href="/search/cs?searchtype=author&query=Sinclair%2C+M">Matthew Sinclair</a>, <a href="/search/cs?searchtype=author&query=Puyol-Ant%C3%B3n%2C+E">Esther Puyol-Ant贸n</a>, <a href="/search/cs?searchtype=author&query=Niederer%2C+S+A">Steven A Niederer</a>, <a href="/search/cs?searchtype=author&query=Chiribiri%2C+A">Amedeo Chiribiri</a>, <a href="/search/cs?searchtype=author&query=Williams%2C+S+E">Steven E Williams</a>, <a href="/search/cs?searchtype=author&query=Williams%2C+M+C">Michelle C Williams</a>, <a href="/search/cs?searchtype=author&query=Young%2C+A+A">Alistair A Young</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="2408.07532v1-abstract-short" style="display: inline;"> Cardiac magnetic resonance (CMR) imaging and computed tomography (CT) are two common non-invasive imaging methods for assessing patients with cardiovascular disease. CMR typically acquires multiple sparse 2D slices, with unavoidable respiratory motion artefacts between slices, whereas CT acquires isotropic dense data but uses ionising radiation. In this study, we explore the combination of Slice S… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.07532v1-abstract-full').style.display = 'inline'; document.getElementById('2408.07532v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.07532v1-abstract-full" style="display: none;"> Cardiac magnetic resonance (CMR) imaging and computed tomography (CT) are two common non-invasive imaging methods for assessing patients with cardiovascular disease. CMR typically acquires multiple sparse 2D slices, with unavoidable respiratory motion artefacts between slices, whereas CT acquires isotropic dense data but uses ionising radiation. In this study, we explore the combination of Slice Shifting Algorithm (SSA), Spatial Transformer Network (STN), and Label Transformer Network (LTN) to: 1) correct respiratory motion between segmented slices, and 2) transform sparse segmentation data into dense segmentation. All combinations were validated using synthetic motion-corrupted CMR slice segmentation generated from CT in 1699 cases, where the dense CT serves as the ground truth. In 199 testing cases, SSA-LTN achieved the best results for Dice score and Huasdorff distance (94.0% and 4.7 mm respectively, average over 5 labels) but gave topological errors in 8 cases. STN was effective as a plug-in tool for correcting all topological errors with minimal impact on overall performance (93.5% and 5.0 mm respectively). SSA also proves to be a valuable plug-in tool, enhancing performance over both STN-based and LTN-based models. The code for these different combinations is available at https://github.com/XESchong/STACOM2024. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.07532v1-abstract-full').style.display = 'none'; document.getElementById('2408.07532v1-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 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">13 pages, STACOM2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.15589">arXiv:2402.15589</a> <span> [<a href="https://arxiv.org/pdf/2402.15589">pdf</a>, <a href="https://arxiv.org/format/2402.15589">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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neural and Evolutionary Computing">cs.NE</span> </div> </div> <p class="title is-5 mathjax"> Prompting LLMs to Compose Meta-Review Drafts from Peer-Review Narratives of Scholarly Manuscripts </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Santu%2C+S+K+K">Shubhra Kanti Karmaker Santu</a>, <a href="/search/cs?searchtype=author&query=Sinha%2C+S+K">Sanjeev Kumar Sinha</a>, <a href="/search/cs?searchtype=author&query=Bansal%2C+N">Naman Bansal</a>, <a href="/search/cs?searchtype=author&query=Knipper%2C+A">Alex Knipper</a>, <a href="/search/cs?searchtype=author&query=Sarkar%2C+S">Souvika Sarkar</a>, <a href="/search/cs?searchtype=author&query=Salvador%2C+J">John Salvador</a>, <a href="/search/cs?searchtype=author&query=Mahajan%2C+Y">Yash Mahajan</a>, <a href="/search/cs?searchtype=author&query=Guttikonda%2C+S">Sri Guttikonda</a>, <a href="/search/cs?searchtype=author&query=Akter%2C+M">Mousumi Akter</a>, <a href="/search/cs?searchtype=author&query=Freestone%2C+M">Matthew Freestone</a>, <a href="/search/cs?searchtype=author&query=Williams%2C+M+C">Matthew C. Williams Jr</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="2402.15589v1-abstract-short" style="display: inline;"> One of the most important yet onerous tasks in the academic peer-reviewing process is composing meta-reviews, which involves understanding the core contributions, strengths, and weaknesses of a scholarly manuscript based on peer-review narratives from multiple experts and then summarizing those multiple experts' perspectives into a concise holistic overview. Given the latest major developments in… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.15589v1-abstract-full').style.display = 'inline'; document.getElementById('2402.15589v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.15589v1-abstract-full" style="display: none;"> One of the most important yet onerous tasks in the academic peer-reviewing process is composing meta-reviews, which involves understanding the core contributions, strengths, and weaknesses of a scholarly manuscript based on peer-review narratives from multiple experts and then summarizing those multiple experts' perspectives into a concise holistic overview. Given the latest major developments in generative AI, especially Large Language Models (LLMs), it is very compelling to rigorously study the utility of LLMs in generating such meta-reviews in an academic peer-review setting. In this paper, we perform a case study with three popular LLMs, i.e., GPT-3.5, LLaMA2, and PaLM2, to automatically generate meta-reviews by prompting them with different types/levels of prompts based on the recently proposed TELeR taxonomy. Finally, we perform a detailed qualitative study of the meta-reviews generated by the LLMs and summarize our findings and recommendations for prompting LLMs for this complex task. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.15589v1-abstract-full').style.display = 'none'; document.getElementById('2402.15589v1-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 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.7 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.13103">arXiv:2312.13103</a> <span> [<a href="https://arxiv.org/pdf/2312.13103">pdf</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="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Exploring Multimodal Large Language Models for Radiology Report Error-checking </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Wu%2C+J">Jinge Wu</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+Y">Yunsoo Kim</a>, <a href="/search/cs?searchtype=author&query=Keller%2C+E+C">Eva C. Keller</a>, <a href="/search/cs?searchtype=author&query=Chow%2C+J">Jamie Chow</a>, <a href="/search/cs?searchtype=author&query=Levine%2C+A+P">Adam P. Levine</a>, <a href="/search/cs?searchtype=author&query=Pontikos%2C+N">Nikolas Pontikos</a>, <a href="/search/cs?searchtype=author&query=Ibrahim%2C+Z">Zina Ibrahim</a>, <a href="/search/cs?searchtype=author&query=Taylor%2C+P">Paul Taylor</a>, <a href="/search/cs?searchtype=author&query=Williams%2C+M+C">Michelle C. Williams</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+H">Honghan Wu</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="2312.13103v2-abstract-short" style="display: inline;"> This paper proposes one of the first clinical applications of multimodal large language models (LLMs) as an assistant for radiologists to check errors in their reports. We created an evaluation dataset from real-world radiology datasets (including X-rays and CT scans). A subset of original reports was modified to contain synthetic errors by introducing three types of mistakes: "insert", "remove",… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.13103v2-abstract-full').style.display = 'inline'; document.getElementById('2312.13103v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.13103v2-abstract-full" style="display: none;"> This paper proposes one of the first clinical applications of multimodal large language models (LLMs) as an assistant for radiologists to check errors in their reports. We created an evaluation dataset from real-world radiology datasets (including X-rays and CT scans). A subset of original reports was modified to contain synthetic errors by introducing three types of mistakes: "insert", "remove", and "substitute". The evaluation contained two difficulty levels: SIMPLE for binary error-checking and COMPLEX for identifying error types. At the SIMPLE level, our fine-tuned model significantly enhanced performance by 47.4% and 25.4% on MIMIC-CXR and IU X-ray data, respectively. This performance boost is also observed in unseen modality, CT scans, as the model performed 19.46% better than the baseline model. The model also surpassed the domain expert's accuracy in the MIMIC-CXR dataset by 1.67%. Notably, among the subsets (N=21) of the test set where a clinician did not achieve the correct conclusion, the LLaVA ensemble mode correctly identified 71.4% of these cases. However, all models performed poorly in identifying mistake types, underscoring the difficulty of the COMPLEX level. This study marks a promising step toward utilizing multimodal LLMs to enhance diagnostic accuracy in radiology. The ensemble model demonstrated comparable performance to clinicians, even capturing errors overlooked by humans. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.13103v2-abstract-full').style.display = 'none'; document.getElementById('2312.13103v2-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> 3 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2204.03408">arXiv:2204.03408</a> <span> [<a href="https://arxiv.org/pdf/2204.03408">pdf</a>, <a href="https://arxiv.org/format/2204.03408">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="Neurons and Cognition">q-bio.NC</span> </div> </div> <p class="title is-5 mathjax"> Surface Vision Transformers: Flexible Attention-Based Modelling of Biomedical Surfaces </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Dahan%2C+S">Simon Dahan</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+H">Hao Xu</a>, <a href="/search/cs?searchtype=author&query=Williams%2C+L+Z+J">Logan Z. J. Williams</a>, <a href="/search/cs?searchtype=author&query=Fawaz%2C+A">Abdulah Fawaz</a>, <a href="/search/cs?searchtype=author&query=Yang%2C+C">Chunhui Yang</a>, <a href="/search/cs?searchtype=author&query=Coalson%2C+T+S">Timothy S. Coalson</a>, <a href="/search/cs?searchtype=author&query=Williams%2C+M+C">Michelle C. Williams</a>, <a href="/search/cs?searchtype=author&query=Newby%2C+D+E">David E. Newby</a>, <a href="/search/cs?searchtype=author&query=Edwards%2C+A+D">A. David Edwards</a>, <a href="/search/cs?searchtype=author&query=Glasser%2C+M+F">Matthew F. Glasser</a>, <a href="/search/cs?searchtype=author&query=Young%2C+A+A">Alistair A. Young</a>, <a href="/search/cs?searchtype=author&query=Rueckert%2C+D">Daniel Rueckert</a>, <a href="/search/cs?searchtype=author&query=Robinson%2C+E+C">Emma C. Robinson</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="2204.03408v1-abstract-short" style="display: inline;"> Recent state-of-the-art performances of Vision Transformers (ViT) in computer vision tasks demonstrate that a general-purpose architecture, which implements long-range self-attention, could replace the local feature learning operations of convolutional neural networks. In this paper, we extend ViTs to surfaces by reformulating the task of surface learning as a sequence-to-sequence learning problem… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.03408v1-abstract-full').style.display = 'inline'; document.getElementById('2204.03408v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2204.03408v1-abstract-full" style="display: none;"> Recent state-of-the-art performances of Vision Transformers (ViT) in computer vision tasks demonstrate that a general-purpose architecture, which implements long-range self-attention, could replace the local feature learning operations of convolutional neural networks. In this paper, we extend ViTs to surfaces by reformulating the task of surface learning as a sequence-to-sequence learning problem, by proposing patching mechanisms for general surface meshes. Sequences of patches are then processed by a transformer encoder and used for classification or regression. We validate our method on a range of different biomedical surface domains and tasks: brain age prediction in the developing Human Connectome Project (dHCP), fluid intelligence prediction in the Human Connectome Project (HCP), and coronary artery calcium score classification using surfaces from the Scottish Computed Tomography of the Heart (SCOT-HEART) dataset, and investigate the impact of pretraining and data augmentation on model performance. Results suggest that Surface Vision Transformers (SiT) demonstrate consistent improvement over geometric deep learning methods for brain age and fluid intelligence prediction and achieve comparable performance on calcium score classification to standard metrics used in clinical practice. Furthermore, analysis of transformer attention maps offers clear and individualised predictions of the features driving each task. Code is available on Github: https://github.com/metrics-lab/surface-vision-transformers <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.03408v1-abstract-full').style.display = 'none'; document.getElementById('2204.03408v1-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> 7 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">10 pages, 3 figures, Submitted to IEEE Transactions on Medical Imaging</span> </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> 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