Paige

Virchow is the first million-slide foundation model for computational pathology, developed by Paige in collaboration with Microsoft. A 632M-parameter ViT-H model trained using DINOv2 on 1.5 million H&E-stained WSIs from MSKCC (17 tissue types). Demonstrates clinical-grade pan-cancer detection with 0.95 AUC across nine common and seven rare cancers. With less training data, the pan-cancer detector built on Virchow achieves similar performance to tissue-specific clinical-grade models in production, outperforming them on rare cancer variants. Serves as the foundation for Paige's Virchow2 (3M WSIs, multimodal) and Virchow2G (1.8B parameters) models.

The analysis of histopathology images with artificial intelligence aims to enable clinical decision support systems and precision medicine. 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 enables pan-cancer detection, achieving 0.95 specimen-level AUC across nine common and seven rare cancers. With less training data, the pan-cancer detector built on Virchow achieved similar performance to tissue-specific clinical-grade models in production and outperformed them on some rare variants of cancer.