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\u2009\u00d7\u2009256 pathology image tiles in 171,189 whole slides from Providence, a large US health network comprising 28 cancer centres.<\/p>\n","protected":false},"excerpt":{"rendered":"
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\u2009\u00d7\u2009256 pathology 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