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September 29, 2025Open Access

Multi-Modal Foundation Models for Computational Pathology: A Survey

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This survey reviews state-of-the-art multi-modal models in computational pathology, highlighting integration of images and textual data.

Multi-modal foundation models enhance analysis of histopathological images, integrating data from various sources.
The survey categorizes 32 models into vision-language, vision-knowledge graph, and vision-gene expression paradigms.
28 multi-modal datasets tailored for pathology are analyzed, emphasizing their significance for training models.
Challenges and future directions in the integration of pathology and AI are identified, guiding further research.

Li et al. (2025) studied this question.

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September 29, 2025Open Access

View Full Paper

Ask AI

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Authors

This survey reviews state-of-the-art multi-modal models in computational pathology, highlighting integration of images and textual data.

Multi-modal foundation models enhance analysis of histopathological images, integrating data from various sources.
The survey categorizes 32 models into vision-language, vision-knowledge graph, and vision-gene expression paradigms.
28 multi-modal datasets tailored for pathology are analyzed, emphasizing their significance for training models.
Challenges and future directions in the integration of pathology and AI are identified, guiding further research.

Li et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark