ArticleNature medicine2024
A foundation model for clinical-grade computational pathology and rare cancers detection.
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 295 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
295 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning prediction of MSI/dMMR, EBV, HER2, PD-L1, and CLDN18.2 status directly from hematoxylin and eosin-stained slides in gastric and gastroesophageal junction adenocarcinoma: a systematic review.Frontiers in oncology · 2026Pooled it
- Whole-slide imaging in hematopathology: Current state, challenges and future opportunities.Journal of pathology informatics · 2026Review
- A new era in computational pathology: A survey on foundation and vision-language models.Journal of pathology informatics · 2026Review
- Towards reliable morphological subtyping and cancer detection on Barrett's esophagus pathology through ensembling foundation models.NPJ digital medicine · 2026Article
- Serial H&E Pathology Reveals Progression-Associated Nuclear Size Drift as a Longitudinal Digital Readout of Bladder Cancer Evolution.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- CSNet: Cognitive Simulation Network for Reliable Histopathology-Based Cancer Diagnosis.Journal of imaging informatics in medicine · 2026Article
- Ensemble learning of pathology foundation models for precision oncology.Cancer cell · 2026Article
- PathScaleBench: A Multidimensional Benchmark of Cross-Scale Representation Behavior and Magnification-Shift Robustness in Pathology Foundation Models.Journal of imaging informatics in medicine · 2026Article
- Exploratory Comparison of Meld, Meld-Na, and Meld 3.0 Scores for Prognostic Assessment at Diagnosis in Hepatocellular Carcinoma Using Machine Learning Approaches.Digestive diseases and sciences · 2026Article
- A universal visual foundation model for computational cytopathology.Nature cancer · 2026Article
- Evaluating the Transferability of Pathology Foundation Models Across Cancer-related H&E Neurodegeneration-related Immunohistochemical Classification Tasks.bioRxiv : the preprint server for biology · 2026Article
- Separating Ranking Signal, Incremental Feature Value, and Evaluation-Induced Optimism in Breast MRI Treatment-Effect Analysis: Foundation Model and Radiomic Representations.Journal of imaging informatics in medicine · 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
- Artificial intelligence in biomarker discovery for diseases: diagnostic and therapeutic prospects.Signal transduction and targeted therapy · 2026Review
- A Reproducible and Explainable Deep Learning Framework for Dermoscopic Image Analysis in Skin Cancer.Journal of clinical medicine · 2026Article
- Biomarkers in Clear Cell Renal Cell Carcinoma: From Biological Association to Clinical Decision-Making.Genes · 2026Review
- Review
- Multimodal Artificial Intelligence in Lung Cancer: From Data Integration to Precision Oncology.Cancers · 2026Review
- A Boveri perspective on cancer biomarker testing using artificial intelligence.NPJ precision oncology · 2026Review
- The Missing Millions: Building Cancer Pathology Artificial Intelligence for Safety Net Populations.Mayo Clinic proceedings. Digital health · 2026Review
235 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
33 authors.
Funding
Abstract
The analysis of histopathology images with artificial intelligence aims to enable clinical decision support systems and precision medicine. The success of such applications depends on the ability to model the diverse patterns observed in pathology images. To this end, we present Virchow, the largest foundation model for computational pathology to date. In addition to the evaluation of biomarker prediction and cell identification, we demonstrate that a large foundation model enables pan-cancer detection, achieving 0.95 specimen-level area under the (receiver operating characteristic) curve across nine common and seven rare cancers. Furthermore, we show that with less training data, the pan-cancer detector built on Virchow can achieve similar performance to tissue-specific clinical-grade models in production and outperform them on some rare variants of cancer. Virchow's performance gains highlight the value of a foundation model and open possibilities for many high-impact applications with limited amounts of labeled training data.
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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.