[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-329172-105":59,"doc-detail-329172-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","a-foundation-model-for-clinical-grade-computational-pathology-and-rare-cancers-detection","A foundation model for clinical-grade computational pathology and rare cancers detection","","Clinical pathology analysis of histopathology images depends on learning diverse patterns so that AI can support clinical decision systems and precision medicine. The work introduces Virchow, a foundation model for computational pathology, and evaluates biomarker prediction and cell identification. It further enables pan-cancer detection, reaching 0.95 specimen-level area under the ROC curve across nine common and seven rare cancers. With less training data, the pan-cancer model matches clinical-grade tissue-specific production performance and surpasses them on some rare variants.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/a-foundation-model-for-clinical-grade-computational-pathology-and-rare-cancers-detection/329172/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/a-foundation-model-for-clinical-grade-computational-pathology-and-rare-cancers-detection/329172.png","ImageObject",300,407,{"name":92,"@type":93},"River Wang","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-22","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is Virchow and what problem does it address?","Question",{"text":112,"@type":113},"Virchow is a foundation model for computational pathology designed to model diverse patterns in histopathology images to enable clinical decision support and precision medicine.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does Virchow perform in rare cancer detection?",{"text":117,"@type":113},"Virchow achieves 0.95 specimen-level area under the ROC curve across nine common and seven rare cancers.",{"name":119,"@type":110,"acceptedAnswer":120},"Can Virchow work with less training data than tissue-specific models?",{"text":121,"@type":113},"Yes. The pan-cancer detector built on Virchow can reach similar performance to tissue-specific clinical-grade models in production while outperforming them on some rare cancer variants.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},329172,1790491261,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":26},1099514067438,"https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542","nature medicine  \nArticle [https://doi.org/10.1038/s41591-024-03141-0](https://doi.org/10.1038/s41591-024-03141-0)  \nA foundation model for clinical-grade computational pathology and rare cancers detection  \nReceived: 6 February 2024  \nAccepted: 19 June 2024  \n\n| Published online: 22 July 2024 |\n| --- |\n|  Check for updates |\n\nEugene Vorontsov  1,6, Alican Bozkurt1,6, Adam Casson1,6, GeorgeShaikovski1,6, Michal Zelechowski1,6, Kristen Severson  2,6, Eric Zimmermann2, James Hall2, Neil Tenenholtz  2, Nicolo Fusi  2, Ellen Yang3, Philippe Mathieu1, Alexander van Eck1, Donghun Lee1, Julian Viret1, Eric Robert  1, Yi Kan Wang1, Jeremy D. Kunz1, Matthew C. H. Lee1, Jan H. Bernhard1, RanA. Godrich1, Gerard Oakley1, Ewan Millar4, Matthew Hanna3, Hannah Wen3,  \nJuanA. Retamero1, William A. Moye1, Razik Yousfi1, Christopher Kanan  1,5, DavidS. Klimstra1, Brandon Rothrock  1, Siqi Liu  1  & Thomas J. Fuchs1  \nThe analysis of histopathology images with artificial intelligence aims to enable clinical decision support systems and precision medicine. The success of such applications depends on the ability to model the diverse patterns observed in pathology images. To this end, we present Virchow, the largest foundation model for computational pathology to date. In addition to the evaluation of biomarker prediction and cell identification, we demonstrate that a large foundation model enablespan-cancer detection, achieving 0.95 specimen-level area under the (receiver operating characteristic) curve across nine common and seven rare cancers. Furthermore, we show that with less training data, the pan-cancer detector built on Virchow can achieve similar performance to tissue-specific clinical-grade models in production and outperform them on some rare variants of cancer. Virchow’s performance gains highlight the value of a foundation model and open possibilities for many high-impact applications with limited amounts of labeled training data.  \nPathologic analysis of tissue is essential for the diagnosis and treatment of cancer. Increasingly, the traditional histological preparations used for light microscopy examination are being replaced by their digital counterparts, also known as whole-slide images (WSIs), which enables the use of computational pathology1–4to move from primarily academic proof points to routine tools in clinical practice. Computational pathology applies artificial intelligence (AI) to digitized WSIsto support the diagnosis, characterization and understanding of disease5,6. Initial work has focused on clinical decision support tools  \nto enhance current workflows7–14, and in 2021 the first Food and Drug Administration-approved AI pathology system was launched10. However, given the incredible gains in performance of computer vision, a subfield of AI focused on images, more recent studies15–19 attempt to unlock new insights from routine WSIsand reveal undiscovered outcomes such as prognosis and therapeutic response20. If successful, such efforts would enhance the utility of hematoxylin and eosin (H&E)-stained WSIsand reduce reliance on specialized and often expensive immunohistochemistry (IHC) or genomic testing21.  \n1Paige, New York, NY, US. 2Microsoft Research, Cambridge, MA, US. 3Memorial Sloan Kettering Cancer Center, New York, NY, US. 4NSW Health Pathology, St George Hospital, Sydney, New South Wales, Australia. 5University of Rochester, Rochester, NY, US. 6These authors contributed equally: Eugene Vorontsov, Alican Bozkurt, Adam Casson, George Shaikovski, Michal Zelechowski, Kristen Severson. e-mail: siqi. liu@paige.ai  \na  \nb  \nc  \ne  \n119,629 patients Unique individuals represented in the data  \n208,815 cases Patient events requiring tissue samples  \n392,268 specimens Tissue samples  \n1,207,837 blocks Para􀂏in-embedded samples sliced for microscopy  \n1,488,550 H&E slides Diagnostic sample tens of thousands of square pixels after digitization  \n≥2.2% neoplasm  \n\n|  |  |  |  |\n| --- | --- | --- | --- |\n| Cancer | Precursor ","cbCaik4Gnyy8ZGds","https://ap.wps.com/l/cbCaik4Gnyy8ZGds","pdf",5101288,24,"English","# Overview of Virchow\n## Foundation model for computational pathology\n## Pan-cancer detection performance","[{\"question\":\"What is Virchow and what problem does it address?\",\"answer\":\"Virchow is a foundation model for computational pathology designed to model diverse patterns in histopathology images to enable clinical decision support and precision medicine.\"},{\"question\":\"How does Virchow perform in rare cancer detection?\",\"answer\":\"Virchow achieves 0.95 specimen-level area under the ROC curve across nine common and seven rare cancers.\"},{\"question\":\"Can Virchow work with less training data than tissue-specific models?\",\"answer\":\"Yes. The pan-cancer detector built on Virchow can reach similar performance to tissue-specific clinical-grade models in production while outperforming them on some rare cancer variants.\"}]","A foundation model for clinical-grade computational pathology and rare cancers detection | PDF",1789982877]