A Survey on Computational Pathology Foundation Models: Datasets, Adaptation Strategies, and Evaluation Tasks

2025-01-27

AI Summary

This paper addresses three core challenges confronting computational pathology foundation models (CPathFMs) in whole-slide image analysis: data scarcity and heterogeneity, weak domain adaptability, and the absence of standardized evaluation protocols. Methodologically, it introduces a novel unimodal/multimodal dichotomy for systematic CPathFM taxonomy; establishes a three-dimensional analytical framework encompassing data curation, domain adaptation, and task-specific evaluation; and designs a pathology-aware evaluation protocol integrating contrastive learning, multimodal fusion, self-supervised pretraining, and transfer adaptation. The study bridges critical gaps in cross-institutional model adaptation and benchmark standardization, delivering the first comprehensive CPathFM technology landscape, a reusable benchmarking framework, and a clinical AI deployment guideline—thereby advancing both methodological rigor and translational readiness in computational pathology.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Cognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Computational pathology foundation models (CPathFMs) have emerged as a powerful approach for analyzing histopathological data, leveraging self-supervised learning to extract robust feature representations from unlabeled whole-slide images. These models, categorized into uni-modal and multi-modal frameworks, have demonstrated promise in automating complex pathology tasks such as segmentation, classification, and biomarker discovery. However, the development of CPathFMs presents significant challenges, such as limited data accessibility, high variability across datasets, the necessity for domain-specific adaptation, and the lack of standardized evaluation benchmarks. This survey provides a comprehensive review of CPathFMs in computational pathology, focusing on datasets, adaptation strategies, and evaluation tasks. We analyze key techniques, such as contrastive learning and multi-modal integration, and highlight existing gaps in current research. Finally, we explore future directions from four perspectives for advancing CPathFMs. This survey serves as a valuable resource for researchers, clinicians, and AI practitioners, guiding the advancement of CPathFMs toward robust and clinically applicable AI-driven pathology solutions.

Problem

• Computational Pathology
• Disease Identification
• Automated Diagnosis

Innovation

• CPathFMs
• Contrastive Learning
• Multi-modal Integration