ArticleNature communications2026
A generalist biomedical vision-language model via multi-CLIP knowledge distillation.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Article
- An efficient 3D latent diffusion model for T1-contrast enhanced MRI generation.Biomedical physics & engineering express · 2026Article
- Prostate Cancer Classification Using Quantum Machine Learning on Multi-parametric MRI.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- A Pilot Study of Multimodal Dosiomics and Longitudinal Delta-Radiomics for Predicting Radiation-Induced Xerostomia in Head-and-Neck Cancer.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- Res-MoCoDiff: residual-guided diffusion models for motion artifact correction in brain MRI.Physics in medicine and biology · 2025Article
- Res-MoCoDiff: Residual-guided diffusion models for motion artifact correction in brain MRI.ArXiv · 2025Article
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Authors and funding
12 authors.
Funding
Abstract
Contrastive Language-Image Pretraining (CLIP) models, which are pretrained on natural images with billions of image-text pairs, exhibit strong zero-shot and cross-modal capabilities. However, their application in biomedicine remains challenging due to limited large-scale image-text data and heterogeneous imaging modalities. Here we show that a generalist biomedical foundation model can be effectively built via multimodal medical knowledge distillation. We introduce MMKD-CLIP, which integrates complementary knowledge from nine biomedical CLIP models. Our two-stage pipeline combines CLIP-style pretraining on 2.9 million biomedical image-text pairs across 26 modalities with large-scale feature-level distillation. We evaluate MMKD-CLIP on 58 datasets spanning nine modalities and six tasks, including classification, retrieval, visual question answering, survival prediction, and cancer diagnosis. MMKD-CLIP performs favorably relative to the teacher models, with results supporting its robustness and cross-domain generalization.
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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 Socratic graph.