Microsoft

Prov-GigaPath by Microsoft Research, Providence, and UW is a whole-slide pathology foundation model pretrained on 1.3 billion 256x256 image tiles from 171,189 whole slides across 28 cancer centers (>30,000 patients, 31 tissue types). Uses a novel GigaPath vision transformer with dilated self-attention (LongNet) for gigapixel-level context. Achieves SOTA on 25/26 benchmark tasks including cancer subtyping, mutation prediction, and TMB classification. The first large-scale whole-slide foundation model trained on real-world clinical data.

Digital pathology poses unique computational challenges, as a standard gigapixel slide may comprise tens of thousands of image tiles. Prior models have often resorted to subsampling a small portion of tiles for each slide, thus missing important slide-level context. Here we present Prov-GigaPath, a whole-slide pathology foundation model pretrained on 1.3 billion pathology image tiles in 171,189 whole slides from Providence, a large US health network comprising 28 cancer centres. To pretrain Prov-GigaPath, we propose GigaPath, a novel vision transformer for pretraining gigapixel pathology slides using dilated self-attention. Prov-GigaPath attains state-of-the-art performance on 25 out of 26 benchmark tasks.

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.