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

TL;DR

This survey reviews the development, datasets, adaptation strategies, and evaluation tasks of computational pathology foundation models, highlighting challenges and future directions for robust AI-driven pathology analysis.

Contribution

It provides a comprehensive overview of CPathFMs, analyzing key techniques, challenges, and gaps, guiding future research in the field.

Findings

Contrastive learning enhances feature robustness.

Multi-modal integration improves diagnostic accuracy.

Standardized benchmarks are lacking for model evaluation.

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.