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September 20, 2025

A Survey of Pathology Foundation Model: Progress and Future Directions

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Authors

CXConghao XiongHCHao ChenJSJoseph J.�Y. Sung

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Overview

This survey categorizes PFM evaluation tasks and identifies challenges in computational pathology, suggesting improvements.

Key Points

  • Pathology foundation models significantly enhance automated cancer diagnosis by improving feature extraction and aggregation.
  • Evaluation tasks for PFMs are classified into slide-level, patch-level, multimodal, and biological tasks to establish benchmarking criteria.
  • Challenges in PFM development include pathology-specific methodology, data scalability, and end-to-end pretraining considerations.
  • Identifying effective adaptation and model maintenance is crucial for utilizing PFMs in computational pathology applications.

Cite This Study

Xiong et al. (2025) studied this question.

synapsesocial.com/papers/68d469d631b076d99fa66df1https://doi.org/10.24963/ijcai.2025/1193
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A survey on computational pathology foundation models: datasets, adaptation strategies, and evaluation tasks2026 · 5 citations
  2. 2Multi-Modal Foundation Models for Computational Pathology: A Survey2025 · 3 citations
  3. 3Pathology Foundation Models2024 · 1 citations
  4. 4Computational pathology in precision oncology: Evolution from task-specific models to foundation models2025 · 9 citations
  5. 5PathBench: A comprehensive comparison benchmark for pathology foundation models towards precision oncology2025 · 3 citations