aceA survey on computational pathology foundation models: datasets, adaptation strategies, and evaluation tasks
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, su…
Development of computational pathology foundation models is constrained by limited data accessibility, high variability across datasets, need for domain-specific adaptation, and lack of standardized evaluation benchmarks
Development constrained by data and evaluation factors including limited accessibility, variability across datasets, need for domain-specific adaptation, and lack of standardized benchmarks
Evidence
- Peer-reviewedKnowledge and Information Systems2026-07-02
- Peer-reviewednpj Digital Medicine2024-09-28
- Peer-reviewedACM Transactions on Computing for Healthcare2022-05-12
- Peer-reviewedNature Communications2025-10-17
- Peer-reviewedJournal of Medical Internet Research2024-08-20
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Truvace Impact Record TRV-2026-0248, v5: “A survey on computational pathology foundation models: datasets, adaptation strategies, and evaluation tasks.” Truvace, 2026-08-02. /record/TRV-2026-0248 (accessed at citation time). sha256 201986be7b07fc16…
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