{"entity":{"id":"paper-xu-nature","kind":"paper","name":"A whole-slide foundation model for digital pathology from real-world data","aka":[],"tldr":"Paper cited by one technology page, indexed on Europe PMC as PubMed record 38778098 and published in Nature; the citing page links this DOI, which is how the record was matched.","summary":"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.\n\nIndexed on Europe PMC as PubMed record 38778098 (DOI 10.1038/s41586-024-07441-w). Matched by DOI alone: one technology page cites this DOI among its external links (the pages are listed under Related), and this page was written so that the citation resolves inside OnCo. No figure has been checked by an editor.","asOf":"2026-09-22","links":[{"label":"Nature 2024","url":"https://doi.org/10.1038/s41586-024-07441-w"},{"label":"PubMed","url":"https://pubmed.ncbi.nlm.nih.gov/38778098/"},{"label":"Europe PMC","url":"https://europepmc.org/article/MED/38778098"}],"tags":["europepmc-ingest"],"related":["prov-gigapath"],"cancers":[],"sections":[],"technologies":[],"targets":[],"drugs":[],"companies":[],"institutions":[],"pathways":[],"terms":[],"trials":[],"people":[],"bottlenecks":[],"keyPapers":[],"journals":["nature"],"dependsOn":[],"notes":[],"journal":"Nature","year":2024,"doi":"10.1038/s41586-024-07441-w","pmid":"38778098","authors":"Xu H, Usuyama N, Bagga J, et al.","paperType":"observational","findings":[],"whatItMeans":"One technology page on OnCo cites this paper by its DOI; this record gives the citation a page of its own so a reader can follow it without leaving OnCo. Read the abstract above alongside the citing page listed under Related; the record was created automatically from the Europe PMC entry and its figures have not been checked by hand.","caveats":["Matched to the citing OnCo records by DOI alone; the summary reproduces the Europe PMC abstract and no figure has been verified against the full paper."]},"route":"/key-papers/paper-xu-nature/","neighbours":{"technology":[{"id":"prov-gigapath","kind":"technology","name":"Prov-GigaPath (Microsoft, Providence)","route":"/technologies/prov-gigapath/"}],"journal":[{"id":"nature","kind":"journal","name":"Nature","route":"/journals/nature/"}]}}