A whole-slide foundation model for digital pathology from real-world data - Xu, Hanwen; Usuyama, Naoto; Bagga, Jaspreet; Zhang, Sheng; Rao, Rajesh; Naumann, Tristan; Wong, Cliff; Gero, Zelalem; González, Javier; Gu, Yu; Xu, Yanbo; Wei, Mu-Hsin; et al. (Nature, 2024)
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 the important slide-level context. Here we present Prov-GigaPath, a whole-slide pathology foundation model pretrained on 1.3 billion 256 × 256 pathology image tiles in 171,189 whole slides from Providence, a large US health network comprising 28 cancer centres.