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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/2304.04142">arXiv:2304.04142</a> <span> [<a href="https://arxiv.org/pdf/2304.04142">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</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"> Slideflow: Deep Learning for Digital Histopathology with Real-Time Whole-Slide Visualization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Dolezal%2C+J+M">James M. Dolezal</a>, <a href="/search/cs?searchtype=author&query=Kochanny%2C+S">Sara Kochanny</a>, <a href="/search/cs?searchtype=author&query=Dyer%2C+E">Emma Dyer</a>, <a href="/search/cs?searchtype=author&query=Srisuwananukorn%2C+A">Andrew Srisuwananukorn</a>, <a href="/search/cs?searchtype=author&query=Sacco%2C+M">Matteo Sacco</a>, <a href="/search/cs?searchtype=author&query=Howard%2C+F+M">Frederick M. Howard</a>, <a href="/search/cs?searchtype=author&query=Li%2C+A">Anran Li</a>, <a href="/search/cs?searchtype=author&query=Mohan%2C+P">Prajval Mohan</a>, <a href="/search/cs?searchtype=author&query=Pearson%2C+A+T">Alexander T. Pearson</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2304.04142v1-abstract-short" style="display: inline;"> Deep learning methods have emerged as powerful tools for analyzing histopathological images, but current methods are often specialized for specific domains and software environments, and few open-source options exist for deploying models in an interactive interface. Experimenting with different deep learning approaches typically requires switching software libraries and reprocessing data, reducing… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.04142v1-abstract-full').style.display = 'inline'; document.getElementById('2304.04142v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2304.04142v1-abstract-full" style="display: none;"> Deep learning methods have emerged as powerful tools for analyzing histopathological images, but current methods are often specialized for specific domains and software environments, and few open-source options exist for deploying models in an interactive interface. Experimenting with different deep learning approaches typically requires switching software libraries and reprocessing data, reducing the feasibility and practicality of experimenting with new architectures. We developed a flexible deep learning library for histopathology called Slideflow, a package which supports a broad array of deep learning methods for digital pathology and includes a fast whole-slide interface for deploying trained models. Slideflow includes unique tools for whole-slide image data processing, efficient stain normalization and augmentation, weakly-supervised whole-slide classification, uncertainty quantification, feature generation, feature space analysis, and explainability. Whole-slide image processing is highly optimized, enabling whole-slide tile extraction at 40X magnification in 2.5 seconds per slide. The framework-agnostic data processing pipeline enables rapid experimentation with new methods built with either Tensorflow or PyTorch, and the graphical user interface supports real-time visualization of slides, predictions, heatmaps, and feature space characteristics on a variety of hardware devices, including ARM-based devices such as the Raspberry Pi. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.04142v1-abstract-full').style.display = 'none'; document.getElementById('2304.04142v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2211.06522">arXiv:2211.06522</a> <span> [<a href="https://arxiv.org/pdf/2211.06522">pdf</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="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> Deep Learning Generates Synthetic Cancer Histology for Explainability and Education </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Dolezal%2C+J+M">James M. Dolezal</a>, <a href="/search/cs?searchtype=author&query=Wolk%2C+R">Rachelle Wolk</a>, <a href="/search/cs?searchtype=author&query=Hieromnimon%2C+H+M">Hanna M. Hieromnimon</a>, <a href="/search/cs?searchtype=author&query=Howard%2C+F+M">Frederick M. Howard</a>, <a href="/search/cs?searchtype=author&query=Srisuwananukorn%2C+A">Andrew Srisuwananukorn</a>, <a href="/search/cs?searchtype=author&query=Karpeyev%2C+D">Dmitry Karpeyev</a>, <a href="/search/cs?searchtype=author&query=Ramesh%2C+S">Siddhi Ramesh</a>, <a href="/search/cs?searchtype=author&query=Kochanny%2C+S">Sara Kochanny</a>, <a href="/search/cs?searchtype=author&query=Kwon%2C+J+W">Jung Woo Kwon</a>, <a href="/search/cs?searchtype=author&query=Agni%2C+M">Meghana Agni</a>, <a href="/search/cs?searchtype=author&query=Simon%2C+R+C">Richard C. Simon</a>, <a href="/search/cs?searchtype=author&query=Desai%2C+C">Chandni Desai</a>, <a href="/search/cs?searchtype=author&query=Kherallah%2C+R">Raghad Kherallah</a>, <a href="/search/cs?searchtype=author&query=Nguyen%2C+T+D">Tung D. Nguyen</a>, <a href="/search/cs?searchtype=author&query=Schulte%2C+J+J">Jefree J. Schulte</a>, <a href="/search/cs?searchtype=author&query=Cole%2C+K">Kimberly Cole</a>, <a href="/search/cs?searchtype=author&query=Khramtsova%2C+G">Galina Khramtsova</a>, <a href="/search/cs?searchtype=author&query=Garassino%2C+M+C">Marina Chiara Garassino</a>, <a href="/search/cs?searchtype=author&query=Husain%2C+A+N">Aliya N. Husain</a>, <a href="/search/cs?searchtype=author&query=Li%2C+H">Huihua Li</a>, <a href="/search/cs?searchtype=author&query=Grossman%2C+R">Robert Grossman</a>, <a href="/search/cs?searchtype=author&query=Cipriani%2C+N+A">Nicole A. Cipriani</a>, <a href="/search/cs?searchtype=author&query=Pearson%2C+A+T">Alexander T. Pearson</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="2211.06522v2-abstract-short" style="display: inline;"> Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or exceeds human pathologists. Discerning how neural networks make their predictions remains a significant challenge, but explainability tools help provide insights into what models have learned when corresponding histologic fea… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.06522v2-abstract-full').style.display = 'inline'; document.getElementById('2211.06522v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2211.06522v2-abstract-full" style="display: none;"> Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or exceeds human pathologists. Discerning how neural networks make their predictions remains a significant challenge, but explainability tools help provide insights into what models have learned when corresponding histologic features are poorly defined. Here, we present a method for improving explainability of DNN models using synthetic histology generated by a conditional generative adversarial network (cGAN). We show that cGANs generate high-quality synthetic histology images that can be leveraged for explaining DNN models trained to classify molecularly-subtyped tumors, exposing histologic features associated with molecular state. Fine-tuning synthetic histology through class and layer blending illustrates nuanced morphologic differences between tumor subtypes. Finally, we demonstrate the use of synthetic histology for augmenting pathologist-in-training education, showing that these intuitive visualizations can reinforce and improve understanding of histologic manifestations of tumor biology. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.06522v2-abstract-full').style.display = 'none'; document.getElementById('2211.06522v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 December, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2022. </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> 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