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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/2410.21256">arXiv:2410.21256</a> <span> [<a href="https://arxiv.org/pdf/2410.21256">pdf</a>, <a href="https://arxiv.org/format/2410.21256">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <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="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> Multi-modal AI for comprehensive breast cancer prognostication </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Witowski%2C+J">Jan Witowski</a>, <a href="/search/cs?searchtype=author&query=Zeng%2C+K">Ken Zeng</a>, <a href="/search/cs?searchtype=author&query=Cappadona%2C+J">Joseph Cappadona</a>, <a href="/search/cs?searchtype=author&query=Elayoubi%2C+J">Jailan Elayoubi</a>, <a href="/search/cs?searchtype=author&query=Chiru%2C+E+D">Elena Diana Chiru</a>, <a href="/search/cs?searchtype=author&query=Chan%2C+N">Nancy Chan</a>, <a href="/search/cs?searchtype=author&query=Kang%2C+Y">Young-Joon Kang</a>, <a href="/search/cs?searchtype=author&query=Howard%2C+F">Frederick Howard</a>, <a href="/search/cs?searchtype=author&query=Ostrovnaya%2C+I">Irina Ostrovnaya</a>, <a href="/search/cs?searchtype=author&query=Fernandez-Granda%2C+C">Carlos Fernandez-Granda</a>, <a href="/search/cs?searchtype=author&query=Schnabel%2C+F">Freya Schnabel</a>, <a href="/search/cs?searchtype=author&query=Ozerdem%2C+U">Ugur Ozerdem</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+K">Kangning Liu</a>, <a href="/search/cs?searchtype=author&query=Steinsnyder%2C+Z">Zoe Steinsnyder</a>, <a href="/search/cs?searchtype=author&query=Thakore%2C+N">Nitya Thakore</a>, <a href="/search/cs?searchtype=author&query=Sadic%2C+M">Mohammad Sadic</a>, <a href="/search/cs?searchtype=author&query=Yeung%2C+F">Frank Yeung</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+E">Elisa Liu</a>, <a href="/search/cs?searchtype=author&query=Hill%2C+T">Theodore Hill</a>, <a href="/search/cs?searchtype=author&query=Swett%2C+B">Benjamin Swett</a>, <a href="/search/cs?searchtype=author&query=Rigau%2C+D">Danielle Rigau</a>, <a href="/search/cs?searchtype=author&query=Clayburn%2C+A">Andrew Clayburn</a>, <a href="/search/cs?searchtype=author&query=Speirs%2C+V">Valerie Speirs</a>, <a href="/search/cs?searchtype=author&query=Vetter%2C+M">Marcus Vetter</a>, <a href="/search/cs?searchtype=author&query=Sojak%2C+L">Lina Sojak</a> , et al. (26 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="2410.21256v1-abstract-short" style="display: inline;"> Treatment selection in breast cancer is guided by molecular subtypes and clinical characteristics. Recurrence risk assessment plays a crucial role in personalizing treatment. Current methods, including genomic assays, have limited accuracy and clinical utility, leading to suboptimal decisions for many patients. We developed a test for breast cancer patient stratification based on digital pathology… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.21256v1-abstract-full').style.display = 'inline'; document.getElementById('2410.21256v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.21256v1-abstract-full" style="display: none;"> Treatment selection in breast cancer is guided by molecular subtypes and clinical characteristics. Recurrence risk assessment plays a crucial role in personalizing treatment. Current methods, including genomic assays, have limited accuracy and clinical utility, leading to suboptimal decisions for many patients. We developed a test for breast cancer patient stratification based on digital pathology and clinical characteristics using novel AI methods. Specifically, we utilized a vision transformer-based pan-cancer foundation model trained with self-supervised learning to extract features from digitized H&E-stained slides. These features were integrated with clinical data to form a multi-modal AI test predicting cancer recurrence and death. The test was developed and evaluated using data from a total of 8,161 breast cancer patients across 15 cohorts originating from seven countries. Of these, 3,502 patients from five cohorts were used exclusively for evaluation, while the remaining patients were used for training. Our test accurately predicted our primary endpoint, disease-free interval, in the five external cohorts (C-index: 0.71 [0.68-0.75], HR: 3.63 [3.02-4.37, p<0.01]). In a direct comparison (N=858), the AI test was more accurate than Oncotype DX, the standard-of-care 21-gene assay, with a C-index of 0.67 [0.61-0.74] versus 0.61 [0.49-0.73], respectively. Additionally, the AI test added independent information to Oncotype DX in a multivariate analysis (HR: 3.11 [1.91-5.09, p<0.01)]). The test demonstrated robust accuracy across all major breast cancer subtypes, including TNBC (C-index: 0.71 [0.62-0.81], HR: 3.81 [2.35-6.17, p=0.02]), where no diagnostic tools are currently recommended by clinical guidelines. These results suggest that our AI test can improve accuracy, extend applicability to a wider range of patients, and enhance access to treatment selection tools. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.21256v1-abstract-full').style.display = 'none'; document.getElementById('2410.21256v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2210.08645">arXiv:2210.08645</a> <span> [<a href="https://arxiv.org/pdf/2210.08645">pdf</a>, <a href="https://arxiv.org/format/2210.08645">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="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> An efficient deep neural network to find small objects in large 3D images </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Park%2C+J">Jungkyu Park</a>, <a href="/search/cs?searchtype=author&query=Ch%C5%82%C4%99dowski%2C+J">Jakub Ch艂臋dowski</a>, <a href="/search/cs?searchtype=author&query=Jastrz%C4%99bski%2C+S">Stanis艂aw Jastrz臋bski</a>, <a href="/search/cs?searchtype=author&query=Witowski%2C+J">Jan Witowski</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+Y">Yanqi Xu</a>, <a href="/search/cs?searchtype=author&query=Du%2C+L">Linda Du</a>, <a href="/search/cs?searchtype=author&query=Gaddam%2C+S">Sushma Gaddam</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+E">Eric Kim</a>, <a href="/search/cs?searchtype=author&query=Lewin%2C+A">Alana Lewin</a>, <a href="/search/cs?searchtype=author&query=Parikh%2C+U">Ujas Parikh</a>, <a href="/search/cs?searchtype=author&query=Plaunova%2C+A">Anastasia Plaunova</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+S">Sardius Chen</a>, <a href="/search/cs?searchtype=author&query=Millet%2C+A">Alexandra Millet</a>, <a href="/search/cs?searchtype=author&query=Park%2C+J">James Park</a>, <a href="/search/cs?searchtype=author&query=Pysarenko%2C+K">Kristine Pysarenko</a>, <a href="/search/cs?searchtype=author&query=Patel%2C+S">Shalin Patel</a>, <a href="/search/cs?searchtype=author&query=Goldberg%2C+J">Julia Goldberg</a>, <a href="/search/cs?searchtype=author&query=Wegener%2C+M">Melanie Wegener</a>, <a href="/search/cs?searchtype=author&query=Moy%2C+L">Linda Moy</a>, <a href="/search/cs?searchtype=author&query=Heacock%2C+L">Laura Heacock</a>, <a href="/search/cs?searchtype=author&query=Reig%2C+B">Beatriu Reig</a>, <a href="/search/cs?searchtype=author&query=Geras%2C+K+J">Krzysztof J. Geras</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="2210.08645v2-abstract-short" style="display: inline;"> 3D imaging enables accurate diagnosis by providing spatial information about organ anatomy. However, using 3D images to train AI models is computationally challenging because they consist of 10x or 100x more pixels than their 2D counterparts. To be trained with high-resolution 3D images, convolutional neural networks resort to downsampling them or projecting them to 2D. We propose an effective alt… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.08645v2-abstract-full').style.display = 'inline'; document.getElementById('2210.08645v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2210.08645v2-abstract-full" style="display: none;"> 3D imaging enables accurate diagnosis by providing spatial information about organ anatomy. However, using 3D images to train AI models is computationally challenging because they consist of 10x or 100x more pixels than their 2D counterparts. To be trained with high-resolution 3D images, convolutional neural networks resort to downsampling them or projecting them to 2D. We propose an effective alternative, a neural network that enables efficient classification of full-resolution 3D medical images. Compared to off-the-shelf convolutional neural networks, our network, 3D Globally-Aware Multiple Instance Classifier (3D-GMIC), uses 77.98%-90.05% less GPU memory and 91.23%-96.02% less computation. While it is trained only with image-level labels, without segmentation labels, it explains its predictions by providing pixel-level saliency maps. On a dataset collected at NYU Langone Health, including 85,526 patients with full-field 2D mammography (FFDM), synthetic 2D mammography, and 3D mammography, 3D-GMIC achieves an AUC of 0.831 (95% CI: 0.769-0.887) in classifying breasts with malignant findings using 3D mammography. This is comparable to the performance of GMIC on FFDM (0.816, 95% CI: 0.737-0.878) and synthetic 2D (0.826, 95% CI: 0.754-0.884), which demonstrates that 3D-GMIC successfully classified large 3D images despite focusing computation on a smaller percentage of its input compared to GMIC. Therefore, 3D-GMIC identifies and utilizes extremely small regions of interest from 3D images consisting of hundreds of millions of pixels, dramatically reducing associated computational challenges. 3D-GMIC generalizes well to BCS-DBT, an external dataset from Duke University Hospital, achieving an AUC of 0.848 (95% CI: 0.798-0.896). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.08645v2-abstract-full').style.display = 'none'; document.getElementById('2210.08645v2-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 February, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2108.04800">arXiv:2108.04800</a> <span> [<a href="https://arxiv.org/pdf/2108.04800">pdf</a>, <a href="https://arxiv.org/format/2108.04800">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"> Meta-repository of screening mammography classifiers </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Stadnick%2C+B">Benjamin Stadnick</a>, <a href="/search/cs?searchtype=author&query=Witowski%2C+J">Jan Witowski</a>, <a href="/search/cs?searchtype=author&query=Rajiv%2C+V">Vishwaesh Rajiv</a>, <a href="/search/cs?searchtype=author&query=Ch%C5%82%C4%99dowski%2C+J">Jakub Ch艂臋dowski</a>, <a href="/search/cs?searchtype=author&query=Shamout%2C+F+E">Farah E. Shamout</a>, <a href="/search/cs?searchtype=author&query=Cho%2C+K">Kyunghyun Cho</a>, <a href="/search/cs?searchtype=author&query=Geras%2C+K+J">Krzysztof J. Geras</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="2108.04800v3-abstract-short" style="display: inline;"> Artificial intelligence (AI) is showing promise in improving clinical diagnosis. In breast cancer screening, recent studies show that AI has the potential to improve early cancer diagnosis and reduce unnecessary workup. As the number of proposed models and their complexity grows, it is becoming increasingly difficult to re-implement them. To enable reproducibility of research and to enable compari… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.04800v3-abstract-full').style.display = 'inline'; document.getElementById('2108.04800v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2108.04800v3-abstract-full" style="display: none;"> Artificial intelligence (AI) is showing promise in improving clinical diagnosis. In breast cancer screening, recent studies show that AI has the potential to improve early cancer diagnosis and reduce unnecessary workup. As the number of proposed models and their complexity grows, it is becoming increasingly difficult to re-implement them. To enable reproducibility of research and to enable comparison between different methods, we release a meta-repository containing models for classification of screening mammograms. This meta-repository creates a framework that enables the evaluation of AI models on any screening mammography data set. At its inception, our meta-repository contains five state-of-the-art models with open-source implementations and cross-platform compatibility. We compare their performance on seven international data sets. Our framework has a flexible design that can be generalized to other medical image analysis tasks. The meta-repository is available at https://www.github.com/nyukat/mammography_metarepository. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.04800v3-abstract-full').style.display = 'none'; document.getElementById('2108.04800v3-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 January, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 August, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 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">17 pages, 2 figures. Meta-repository available at https://www.github.com/nyukat/mammography_metarepository ; v3 adds results on the CSAW-CC dataset</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2008.01774">arXiv:2008.01774</a> <span> [<a href="https://arxiv.org/pdf/2008.01774">pdf</a>, <a href="https://arxiv.org/format/2008.01774">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="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Shamout%2C+F+E">Farah E. Shamout</a>, <a href="/search/cs?searchtype=author&query=Shen%2C+Y">Yiqiu Shen</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+N">Nan Wu</a>, <a href="/search/cs?searchtype=author&query=Kaku%2C+A">Aakash Kaku</a>, <a href="/search/cs?searchtype=author&query=Park%2C+J">Jungkyu Park</a>, <a href="/search/cs?searchtype=author&query=Makino%2C+T">Taro Makino</a>, <a href="/search/cs?searchtype=author&query=Jastrz%C4%99bski%2C+S">Stanis艂aw Jastrz臋bski</a>, <a href="/search/cs?searchtype=author&query=Witowski%2C+J">Jan Witowski</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+D">Duo Wang</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+B">Ben Zhang</a>, <a href="/search/cs?searchtype=author&query=Dogra%2C+S">Siddhant Dogra</a>, <a href="/search/cs?searchtype=author&query=Cao%2C+M">Meng Cao</a>, <a href="/search/cs?searchtype=author&query=Razavian%2C+N">Narges Razavian</a>, <a href="/search/cs?searchtype=author&query=Kudlowitz%2C+D">David Kudlowitz</a>, <a href="/search/cs?searchtype=author&query=Azour%2C+L">Lea Azour</a>, <a href="/search/cs?searchtype=author&query=Moore%2C+W">William Moore</a>, <a href="/search/cs?searchtype=author&query=Lui%2C+Y+W">Yvonne W. Lui</a>, <a href="/search/cs?searchtype=author&query=Aphinyanaphongs%2C+Y">Yindalon Aphinyanaphongs</a>, <a href="/search/cs?searchtype=author&query=Fernandez-Granda%2C+C">Carlos Fernandez-Granda</a>, <a href="/search/cs?searchtype=author&query=Geras%2C+K+J">Krzysztof J. Geras</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2008.01774v2-abstract-short" style="display: inline;"> During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images and a gradient boosting model that learns from routine clinical variables. Our AI prognosis s… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.01774v2-abstract-full').style.display = 'inline'; document.getElementById('2008.01774v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2008.01774v2-abstract-full" style="display: none;"> During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images and a gradient boosting model that learns from routine clinical variables. Our AI prognosis system, trained using data from 3,661 patients, achieves an area under the receiver operating characteristic curve (AUC) of 0.786 (95% CI: 0.745-0.830) when predicting deterioration within 96 hours. The deep neural network extracts informative areas of chest X-ray images to assist clinicians in interpreting the predictions and performs comparably to two radiologists in a reader study. In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time. In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.01774v2-abstract-full').style.display = 'none'; document.getElementById('2008.01774v2-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 November, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 August, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2020. </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" 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