{"type":"ScholarlyArticle","title":"A Survey of Pathology Foundation Model: Progress and Future Directions","authors":[{"name":"Conghao Xiong","slug":"conghao-xiong","given":"Conghao","family":"Xiong","affiliation":""},{"name":"Hao Chen","slug":"hao-chen","given":"Hao","family":"Chen","affiliation":""},{"name":"Joseph J. Y. Sung","slug":"joseph-j-y-sung","given":"Joseph J.Y.","family":"Sung","affiliation":""}],"abstract":"Computational pathology, which involves analyzing whole slide images for automated cancer diagnosis, relies on multiple instance learning, where performance depends heavily on the feature extractor and aggregator. Recent Pathology Foundation Models (PFMs), pretrained on large-scale histopathology data, have significantly enhanced both the extractor and aggregator, but they lack a systematic analysis framework. In this survey, we present a hierarchical taxonomy organizing PFMs through a top-down philosophy applicable to foundation model analysis in any domain: model scope, model pretraining, and model design. Additionally, we systematically categorize PFM evaluation tasks into slide-level, patch-level, multimodal, and biological tasks, providing comprehensive benchmarking criteria. Our analysis identifies critical challenges in both PFM development (pathology-specific methodology, end-to-end pretraining, data-model scalability) and utilization (effective adaptation, model maintenance), paving the way for future directions in this promising field. Resources referenced in this survey are available at https://github.com/BearCleverProud/AwesomeWSI.","doi":"10.24963/ijcai.2025/1193","url":"https://ijcai.org/proceedings/2025/1193","pdf":"https://ijcai.org/proceedings/2025/1193.pdf","markdown":"https://ijcai.org/proceedings/2025/1193.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/1193","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Survey Track","track":"Survey Track","number":1193,"pages":{"first":10751,"last":10760},"keywords":["Computer Vision: CV: Representation learning","Machine Learning: ML: Representation learning","Computer Vision: CV: Machine learning for vision","Machine Learning: ML: Multi-instance learning","Computer Vision: CV: Biomedical image analysis","Machine Learning: ML: Feature extraction, selection and dimensionality reduction"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}