Medicine and Health

A foundation model for clinical-grade computational pathology and rare cancers detection

E. Vorontsov, A. Bozkurt, et al.

Discover Virchow, a groundbreaking foundation model developed by a team of experts including Eugene Vorontsov and Kristen Severson, that excels in pan-cancer detection. Trained with vast data from over 100,000 patients, it achieves outstanding accuracy with rare cancer variants, paving the way for innovative applications in computational pathology.

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Abstract

This paper introduces Virchow, a large foundation model for computational pathology, trained on data from approximately 100,000 patients and 1.5 million H&E-stained whole-slide images. The model demonstrates high performance in pan-cancer detection (0.95 AUC across nine common and seven rare cancers), approaching or exceeding the performance of tissue-specific clinical-grade models with less training data, particularly for rare cancer variants. This highlights the value of foundation models for diverse applications in computational pathology with limited labeled data.

Publisher

Nature Medicine

Published On

Oct 01, 2024

Authors

Eugene Vorontsov, Alican Bozkurt, Adam Casson, George Shaikovski, Michal Zelechowski, Kristen Severson, Eric Zimmermann, James Hall, Neil Tenenholtz, Nicolo Fusi, Ellen Yang, Philippe Mathieu, Alexander van Eck, Donghun Lee, Julian Viret, Eric Robert, Yi Kan Wang, Jeremy D. Kunz, Matthew C. H. Lee, Jan H. Bernhard, Ran A. Godrich, Gerard Oakley, Ewan Millar, Matthew Hanna, Hannah Wen, Juan A. Retamero, William A. Moye, Razik Yousfi, Christopher Kanan, David S. Klimstra, Brandon Rothrock, Siqi Liu, Thomas J. Fuchs

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