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Open access Aug 2026

An explainable biomedical foundation model via large-scale concept-enhanced vision-language pretraining.

Artificial intelligence for medical imaging is required to be accurate and interpretable to clinicians. However, current multimodal biomedical foundation models often prioritize performance over explainability. Here we present ConceptCLIP, an explainable biomedical foundation model that achieves state-of-the-art diagno...

Yuxiang Nie, Sunan He, Yequan Bie et al. · 0 citations
Open access Sep 2026

CellART: a unified framework for extracting single-cell information from high-resolution spatial transcriptomics

This study introduces CellART, a unified framework designed to extract single-cell information across diverse high-resolution spatial transcriptomics platforms, including VisiumHD, Xenium, MERFISH, and Stereo-seq, and highlights its ability to fully leverage high-resolution ST data.

Yu-Heng Chen, Yu-Yao Liu, Zhi-Wei Wang et al. · 1 citation

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