{"entity":{"id":"virchow","kind":"technology","name":"Virchow / Virchow2 (Paige, MSK)","aka":[],"tldr":"A pathology foundation model trained on millions of slides that can detect cancer and predict biomarkers from an ordinary H&E slide.","summary":"Virchow (2024, 632M parameters, 1.5M slides) and Virchow2/2G (2024, up to 1.9B parameters, 3.1M slides from MSK and global sites, mixed magnification) are the largest proprietary pathology foundation models; they underpin Paige's pan-cancer detection and biomarker products and were trained with Microsoft compute.","status":"emerging","asOf":"2026-09-08","links":[{"label":"Virchow2 (arXiv 2024)","url":"https://arxiv.org/abs/2408.00738"},{"label":"Virchow, Nature Medicine 2024","url":"https://doi.org/10.1038/s41591-024-03141-0"}],"tags":["foundation-model","pathology"],"related":[],"cancers":[],"sections":["ai-computation"],"technologies":["pathology-foundation-model","digital-pathology-ai"],"targets":[],"drugs":[],"companies":["paige","microsoft-research"],"institutions":["mskcc"],"pathways":[],"terms":[],"trials":[],"people":[],"bottlenecks":[],"keyPapers":["paper-vorontsov-nat-med"],"journals":[],"dependsOn":[],"notes":[],"principle":"Self-supervised DINOv2 vision transformer pretraining on tissue tiles at several magnifications; frozen encoder plus small task heads.","strengths":["Scale and data diversity","Strong biomarker prediction from H&E"],"limitations":["Proprietary weights","Scanner and stain shift"],"since":2024},"route":"/technologies/virchow/","neighbours":{"section":[{"id":"ai-computation","kind":"section","name":"AI & Computation","route":"/fronts/ai-computation/"}],"technology":[{"id":"digital-pathology-ai","kind":"technology","name":"Digital pathology & AI","route":"/technologies/digital-pathology-ai/"},{"id":"pathology-foundation-model","kind":"technology","name":"Pathology & radiology foundation models","route":"/technologies/pathology-foundation-model/"}],"company":[{"id":"microsoft-research","kind":"company","name":"Microsoft (Research and Health AI)","route":"/companies/microsoft-research/"},{"id":"paige","kind":"company","name":"Paige AI","route":"/companies/paige/"}],"institution":[{"id":"mskcc","kind":"institution","name":"Memorial Sloan Kettering Cancer Center","route":"/institutions/mskcc/"}],"paper":[{"id":"paper-vorontsov-nat-med","kind":"paper","name":"A foundation model for clinical-grade computational pathology and rare cancers detection","route":"/key-papers/paper-vorontsov-nat-med/"}],"roadmap":[{"id":"ai-oncology-roadmap","kind":"roadmap","name":"AI in oncology roadmap: pattern readers → foundation models → agents in the workflow","route":"/roadmaps/ai-oncology-roadmap/"},{"id":"ai-oncology-clinic","kind":"roadmap","name":"AI in the oncology clinic: from narrow cleared tools to multimodal decision support","route":"/roadmaps/ai-oncology-clinic/"},{"id":"diagnostics-roadmap","kind":"roadmap","name":"Diagnostics roadmap: stains → gene panels → blood tests that decide treatment","route":"/roadmaps/diagnostics-roadmap/"}]}}