A whole-slide foundation model for digital pathology from real-world data.

ABSTRACT: Digital pathology poses unique computational challenges, as a standard gigapixel slide may comprise tens of thousands of image tiles^1-3. Prior models have often resorted to subsampling a small portion of tiles for each slide, thus missing the important slide-level context⁴. 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. The slides originated from more than 30,000 patients covering 31 major tissue types. To pretrain Prov-GigaPath, we propose GigaPath, a novel vision transformer architecture for pretraining gigapixel pathology slides. To scale GigaPath for slide-level learning with tens of tho

SUBMITTER: Xu H 

PROVIDER: S-EPMC11153137 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature