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Multimodal Optical Imaging Platform for Studying Cellular Metabolism

Multimodal Optical Imaging Platform for Studying Cellular Metabolism

Published on: June 6, 2025

Multi-Modal Foundation Models for Computational Pathology: A Survey.

Dong Li¹, Guihong Wan², Xintao Wu³

¹Department of Computer Science, Baylor University.

Transactions on Machine Learning Research

|April 17, 2026

Summary

This summary is machine-generated.

Multi-modal foundation models integrate diverse data for computational pathology (CPath). This survey reviews 34 models and 30 datasets, categorizing approaches and outlining future directions for AI in pathology.

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Area of Science:

Computational pathology
Artificial intelligence in medicine
Digital pathology

Background:

Foundation models are advancing computational pathology (CPath) with scalable and generalizable histopathological image analysis.
Recent progress emphasizes multi-modal foundation models integrating visual data with textual reports, domain knowledge, and molecular profiles.

Purpose of the Study:

To provide a comprehensive review of multi-modal foundation models in CPath.
To focus on models utilizing hematoxylin and eosin (H&E) stained whole slide images (WSIs) and tile-level data.
To categorize existing models, datasets, tasks, and strategies, identifying future research avenues.

Main Methods:

Categorization of 34 state-of-the-art multi-modal foundation models into vision-language, vision-knowledge graph, and vision-gene expression paradigms.
Further classification of vision-language models into non-LLM-based and LLM-based approaches.
Analysis and grouping of 30 pathology-focused multi-modal datasets into image-text, instruction, and image-other modality pairs.

Main Results:

Identified three primary multi-modal foundation model paradigms: vision-language, vision-knowledge graph, and vision-gene expression.
Cataloged 34 distinct multi-modal foundation models and 30 relevant datasets.
Developed a taxonomy of downstream tasks and analyzed training/evaluation strategies.

Conclusions:

Multi-modal foundation models represent a significant advancement in computational pathology.
This survey offers a structured overview and resource for researchers in AI and pathology.
Key challenges and future directions are highlighted to guide further development in the field.