{"entity":{"id":"paper-lu-nat-med","kind":"paper","name":"A visual-language foundation model for computational pathology","aka":[],"tldr":"Paper cited by one technology page, indexed on Europe PMC as PubMed record 38504017 and published in Nature Medicine; the citing page links this DOI, which is how the record was matched.","summary":"The accelerated adoption of digital pathology and advances in deep learning have enabled the development of robust models for various pathology tasks across a diverse array of diseases and patient cohorts. However, model training is often difficult due to label scarcity in the medical domain, and a model's usage is limited by the specific task and disease for which it is trained. Additionally, most models in histopathology leverage only image data, a stark contrast to how humans teach each other and reason about histopathologic entities. We introduce CONtrastive learning from Captions for Histopathology (CONCH), a visual-language foundation model developed using diverse sources of histopathology images, biomedical text and, notably, over 1.17 million image-caption pairs through task-agnostic pretraining. Evaluated on a suite of 14 diverse benchmarks, CONCH can be transferred to a wide range of downstream tasks involving histopathology images and/or text, achieving state-of-the-art performance on histology image classification, segmentation, captioning, and text-to-image and image-to-text retrieval. CONCH represents a substantial leap over concurrent visual-language pretrained systems for histopathology, with the potential to directly facilitate a wide array of machine learning-based workflows requiring minimal or no further supervised fine-tuning.\n\nIndexed on Europe PMC as PubMed record 38504017 (DOI 10.1038/s41591-024-02856-4). 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":"Nat Med 2024","url":"https://doi.org/10.1038/s41591-024-02856-4"},{"label":"PubMed","url":"https://pubmed.ncbi.nlm.nih.gov/38504017/"},{"label":"Europe PMC","url":"https://europepmc.org/article/MED/38504017"}],"tags":["europepmc-ingest"],"related":["uni-conch"],"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/s41591-024-02856-4","pmid":"38504017","authors":"Lu MY, Chen B, Williamson DFK, 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-lu-nat-med/","neighbours":{"technology":[{"id":"uni-conch","kind":"technology","name":"UNI and CONCH (Harvard, Mahmood Lab)","route":"/technologies/uni-conch/"}],"journal":[{"id":"nature-medicine","kind":"journal","name":"Nature Medicine","route":"/journals/nature-medicine/"}]}}