{"entity":{"id":"paper-chen-nat-med","kind":"paper","name":"Towards a general-purpose foundation model for computational pathology","aka":[],"tldr":"Paper cited by two technology pages and one idea page, indexed on Europe PMC as PubMed record 38504018 and published in Nature Medicine; the citing pages link this DOI, which is how the record was matched.","summary":"Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks, requiring the objective characterization of histopathological entities from whole-slide images (WSIs). The high resolution of WSIs and the variability of morphological features present significant challenges, complicating the large-scale annotation of data for high-performance applications. To address this challenge, current efforts have proposed the use of pretrained image encoders through transfer learning from natural image datasets or self-supervised learning on publicly available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale. We introduce UNI, a general-purpose self-supervised model for pathology, pretrained using more than 100 million images from over 100,000 diagnostic H&E-stained WSIs (>77 TB of data) across 20 major tissue types. The model was evaluated on 34 representative CPath tasks of varying diagnostic difficulty. In addition to outperforming previous state-of-the-art models, we demonstrate new modeling capabilities in CPath such as resolution-agnostic tissue classification, slide classification using few-shot class prototypes, and disease subtyping generalization in classifying up to 108 cancer types in the OncoTree classification system. UNI advances unsupervised representation learning at scale in CPath in terms of both pretraining data and downstream evaluation, enabling data-efficient artificial intelligence models that can generalize and transfer to a wide range of diagnostically challenging tasks and clinical workflows in anatomic pathology.\n\nIndexed on Europe PMC as PubMed record 38504018 (DOI 10.1038/s41591-024-02857-3). Matched by DOI alone: two technology pages and one idea page cite this DOI among their 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":"Nat Med 2024","url":"https://doi.org/10.1038/s41591-024-02857-3"},{"label":"PubMed","url":"https://pubmed.ncbi.nlm.nih.gov/38504018/"},{"label":"Europe PMC","url":"https://europepmc.org/article/MED/38504018"}],"tags":["europepmc-ingest"],"related":["pathology-foundation-model","uni-conch","idea-multimodal-foundation-model"],"cancers":[],"sections":[],"technologies":[],"targets":[],"drugs":[],"companies":[],"institutions":[],"pathways":[],"terms":[],"trials":[],"people":[],"bottlenecks":[],"keyPapers":[],"journals":["nature-medicine"],"dependsOn":[],"notes":[],"journal":"Nature Medicine","year":2024,"doi":"10.1038/s41591-024-02857-3","pmid":"38504018","authors":"Chen RJ, Ding T, Lu MY, et al.","paperType":"observational","findings":[],"whatItMeans":"Two technology pages and one idea page on OnCo cite 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 pages 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-chen-nat-med/","neighbours":{"technology":[{"id":"pathology-foundation-model","kind":"technology","name":"Pathology & radiology foundation models","route":"/technologies/pathology-foundation-model/"},{"id":"uni-conch","kind":"technology","name":"UNI and CONCH (Harvard, Mahmood Lab)","route":"/technologies/uni-conch/"}],"idea":[{"id":"idea-multimodal-foundation-model","kind":"idea","name":"Patient-level multimodal foundation models for treatment selection","route":"/ideas/idea-multimodal-foundation-model/"}],"journal":[{"id":"nature-medicine","kind":"journal","name":"Nature Medicine","route":"/journals/nature-medicine/"}]}}