ArticleNature communications2025
A multimodal knowledge-enhanced whole-slide pathology foundation model.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- 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
- End-to-end multimodal pathology foundation model with clinical dialogue.Nature medicine · 2026Article
- Review
- Leveraging multi-modal foundation models for analysing spatial multi-omic and histopathology data.Nature biomedical engineering · 2026Article
- A unified vision-language model for precision oncology and biomarker prediction in neuroblastoma.Nature communications · 2026Article
- Pathology-derived clinical micro-architectural diagnostics of tumour-microbiome interactions in colorectal cancer.Journal of translational medicine · 2026Article
- A CLIP-based framework for multiclass lung histopathology classification with prompt engineering and class-imbalance-aware focal optimization.Scientific reports · 2026Article
- Generalist biological artificial intelligence in modeling the language of life.Nature biotechnology · 2026Review
- Towards generalizable AI in medicine via Generalist-Specialist Collaboration.Nature biomedical engineering · 2026Article
- Boosting pathology foundation models via few-shot prompt-tuning for rare cancer subtyping.Nature communications · 2026Article
- PRET is a few-shot system for pan-cancer recognition without example training.Nature cancer · 2026Article
- A cancer-type-aware framework for robust multimodal survival prediction under missing modalities.Briefings in bioinformatics · 2026Article
- A Clinically Translatable Multimodal Deep Learning Model for HRD Detection from Histopathology Images.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial intelligence in digital pathology diagnosis and analysis: technologies, challenges, and future prospects.Military Medical Research · 2026Review
- Foundation Models in Cancer Pathology: Techniques, Applications, and Future Directions.Research (Washington, D.C.) · 2026Review
- Multimodal artificial intelligence and machine learning in oncology: from data integration to precision cancer care.Frontiers in digital health · 2026Review
- Foundation models in computational pathology: methods, applications and clinical implications.BMJ oncology · 2026Review
- Multimodal AI in precision medicine: linking omics, imaging and clinical decisions.American journal of clinical and experimental immunology · 2026Article
- Foundation models: Insights and implications for gastrointestinal cancer.World journal of gastroenterology · 2025Review
- A multimodal knowledge-enhanced whole-slide pathology foundation model.Nature communications · 2025Article
Corrections and comments
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Authors and funding
19 authors.
Funding
Abstract
Computational pathology has advanced through foundation models, yet faces challenges in multimodal integration and capturing whole-slide context. Current approaches typically utilize either vision-only or image-caption data, overlooking distinct insights from pathology reports and gene expression profiles. Additionally, most models focus on patch-level analysis, failing to capture comprehensive whole-slide patterns. Here we present mSTAR (Multimodal Self-TAught PRetraining), the pathology foundation model that incorporates three modalities: pathology slides, expert-created reports, and gene expression data, within a unified framework. Our dataset includes 26,169 slide-level modality pairs across 32 cancer types, comprising over 116 million patch images. This approach injects multimodal whole-slide context into patch representations, expanding modeling from single to multiple modalities and from patch-level to slide-level analysis. Across oncological benchmark spanning 97 tasks, mSTAR outperforms previous state-of-the-art models, particularly in molecular prediction and multimodal tasks, revealing that multimodal integration yields greater improvements than simply expanding vision-only datasets.
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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.