This survey reviews computational pathology foundation models, focusing on data, adaptation, and evaluation strategies, indicating future directions for AI advancements.
Key Points
The survey aims to review computational pathology foundation models (CPathFMs) and their capabilities, challenges, and future prospects.
Comprehensive review of CPathFMs with a focus on datasets, adaptation strategies, and evaluation tasks.
Analysis of techniques like contrastive learning and multi-modal integration.
Identification of gaps and future directions in computational pathology.
Highlight the promise of CPathFMs in pathology tasks such as segmentation and classification.
Point out challenges including data accessibility and variability across datasets.
Suggest future avenues for CPathFMs to improve clinical applications.