ArticleNature medicine2024
A visual-language foundation model for computational pathology.
Article in Nature medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 412 papers.
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Who cites it
412 citing papers in PubMed.
- A multimodal AI biomarker PATH-ORACLE improves prediction of recurrence in stage I lung adenocarcinoma.Immuno-oncology technology · 2026Article
- A new era in computational pathology: A survey on foundation and vision-language models.Journal of pathology informatics · 2026Review
- CellPrior-net: Prior-guided nuclei detection and classification for H&E whole-slide images.Journal of pathology informatics · 2026Article
- HistoEncoder: A digital pathology foundation model for prostate cancer.Journal of pathology informatics · 2026Article
- DeepLNM: A Multimodal Model for Predicting Lymph Node Metastasis in Colorectal Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Mitigating Domain Shift and Dataset Bias in Deep Learning Models for Cancer Diagnostics.Journal of imaging informatics in medicine · 2026Review
- Ensemble learning of pathology foundation models for precision oncology.Cancer cell · 2026Article
- Differential cardiac remodelling: patterns of cardiomyocyte morphometry in multiple patient conditions.Cardiovascular research · 2026Article
- From traditional histopathology to virtual staining: deep learning approaches for assessing tissue lesions in farmed fish.Veterinary research communications · 2026Review
- A Facial Foundation Model for Clinical Biomarker Prediction and Real-World Mobile Deployment.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A Generalizable Multimodal Model for Treatment-Stratified Risk and Survival Assessment under Real-World Constraints: A Multi-Center Study of Colorectal Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A universal visual foundation model for computational cytopathology.Nature cancer · 2026Article
- Article
- CLEAR-HPV: Interpretable concept discovery for human-papillomavirus-associated morphology in whole-slide histology.Patterns (New York, N.Y.) · 2026Article
- Leakage-Controlled Classification of Dataset-Derived Ordinal p53-Signature Size Categories in Fallopian-Tube Immunohistochemistry.Diagnostics (Basel, Switzerland) · 2026Article
- Generative large language models in medicine: a scoping review of recent methodological advances.npj health systems · 2026Review
- Counterfactual Diffusion Models Provide Interpretable Explanations of Artificial Intelligence Models in Pathology.Cancer research · 2026Article
- Artificial intelligence for detection, grading, and prognostication in prostate cancer pathology: A scoping review.Histology and histopathology · 2026Article
- SlideChat is a multimodal generative artificial intelligence assistant for whole-slide computational pathology across cancer types.Nature cancer · 2026Article
- Foundation model-powered deep learning of endometrial histology for predicting the cumulative live birth of an in vitro fertilization cycle.PLOS digital health · 2026Article
352 more citing papers are in PubMed but not listed here.
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Authors and funding
13 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.