{"id":"doi:10.1038/s41586-024-07441-w","kind":"article","title":"A whole-slide foundation model for digital pathology from real-world data","authors":["Xu H","Usuyama N","Bagga J","Zhang S","Rao R","Naumann T","Wong C","Gero Z","González J","Gu Y","Xu Y","Wei M","Wang W","Ma S","Wei F","Yang J","Li C","Gao J","Rosemon J","Bower T","Lee S","Weerasinghe R","Wright BJ","Robicsek A","Piening B"],"author_count":28,"year":2024,"published":"2024-05-22","venue":"Nature","cited_by":578,"doi":"10.1038/s41586-024-07441-w","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41586-024-07441-w","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41586-024-07441-w","updated":null,"pmid":"38778098","pmcid":"PMC11153137","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC11153137/pdf","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 context 4 . 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 thousands of image tiles, GigaPath adapts the newly developed LongNet 5 method to digital pathology. To evaluate Prov-GigaPath, we construct a digital pathology benchmark comprising 9 cancer subtyping tasks and 17 pathomics tasks, using both Providence and TCGA data 6 . With large-scale pretraining and ultra-large-context modelling, Prov-GigaPath attains state-of-the-art performance on 25 out of 26 tasks, with significant improvement over the second-best method on 18 tasks. We further demonstrate the potential of Prov-GigaPath on vision-language pretraining for pathology 7,8 by incorporating the pathology reports. In sum, Prov-GigaPath is an open-weight foundation model that achieves state-of-the-art performance on various digital pathology tasks, demonstrating the importance of real-world data and whole-slide modelling.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"A whole-slide foundation model for digital pathology from real-world data\" by Xu H, Usuyama N, Bagga J et al., https://doi.org/10.1038/s41586-024-07441-w, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified.","topics":[{"slug":"ai-medicine","name":"AI in Clinical Medicine","domain_slug":"medicine"}],"cited_in_dossiers":[{"slug":"ai-medicine","title":"AI in Clinical Medicine: State of the Field (October 2026)","url":"https://space-kitty.com/dossiers/ai-medicine"}]}