@misc{vorontsov2023virchow,
author = {Vorontsov, Eugene and Bozkurt, A. and Casson, Adam and Shaikovski, George and Zelechowski, Michal and Liu, Siqi and Severson, Kristen and Zimmermann, Eric and Hall, James and Tenenholtz, Neil and Fusi, Nicolo and Mathieu, Philippe and Eck, Alexander van and Lee, Donghun and Viret, Julian and Robert, Eric and Wang, Yi Kan and Kun, Jeremy D. and Le, Matthew C. H. and Bernhard, Jan H and Godrich, R. and Oakley, Gerard and Millar, Ewan and Hanna, Matthew G and Retamero, J. and Moye, William A. and Yousfi, Razik and Kanan, Christopher and Klimstra, D. and Rothrock, B. and Fuchs, Thomas J.},
title = {Virchow: A Million-Slide Digital Pathology Foundation Model},
howpublished = {arXiv},
year = {2023},
month = {September},
abstract = {The use of artificial intelligence to enable precision medicine and decision support systems through the analysis of pathology images has the potential to revolutionize the diagnosis and treatment of cancer. Such applications will depend on models' abilities to capture the diverse patterns observed in pathology images. To address this challenge, we present Virchow, a foundation model for computational pathology. Using self-supervised learning empowered by the DINOv2 algorithm, Virchow is a vision transformer model with 632 million parameters trained on 1.5 million hematoxylin and eosin stained whole slide images from diverse tissue and specimen types, which is orders of magnitude more data than previous works. The Virchow model enables the development of a pan-cancer detection system with 0.949 overall specimen-level AUC across 17 different cancer types, while also achieving 0.937 AUC on 7 rare cancer types. The Virchow model sets the state-of-the-art on the internal and external image tile level benchmarks and slide level biomarker prediction tasks. The gains in performance highlight the importance of training on massive pathology image datasets, suggesting scaling up the data and network architecture can improve the accuracy for many high-impact computational pathology applications where limited amounts of training data are available.},
url = {http://approjects.co.za/?big=en-us/research/publication/virchow-a-million-slide-digital-pathology-foundation-model/},
}