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A Survey of Pathology Foundation Model: Progress and Future Directions
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A Survey of Pathology Foundation Model: Progress and Future Directions
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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.
Forward citations
Cited by 5 Pith papers
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Benchmarking Pathology Foundation Models for Spatial Domain Understanding
SpaPath-Bench evaluates spatial representation in 19 pathology foundation models via spatial domain identification on 42 paired WSI-ST slides using three agreement criteria across 83K runs.
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DaX: Learning General Pathology Representations Across Scales
DaX is a pathology vision foundation model that extends DINOv3 with continuous magnification training and cross-scale consistency, achieving top average performance on a benchmark of 161 tasks from 44 datasets coverin...
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BRIGHT: A Collaborative Generalist-Specialist Foundation Model for Breast Pathology
Fine-tuning a generalist pathology model on 51,000 breast WSIs and concatenating its features with the original model yields top-1 performance on 21 of 24 internal breast-pathology tasks, but only 5 of 10 external tasks.
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Validation of Whole-Slide Foundation Models for Image Retrieval in TCGA Data
Benchmarking on TCGA shows TITAN foundation model edges out others for whole-slide retrieval but with only ~68% average accuracy, high organ-to-organ variation, and no consistent winner over patch-level baselines.
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Beyond the Failures: Rethinking Foundation Models in Pathology
Foundation models stumble in pathology due to conceptual mismatches with biological tissue, requiring explicitly designed models rather than adaptations of natural-image methods.
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