GRACE, a gastric-specific pathology foundation model trained on multicenter HE-stained slides, outperforms pancancer models on 28 tasks and improves pathologist accuracy, speed, and agreement in a reader study while enabling case triaging.
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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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A Pathology Foundation Model for Gastric Cancer with Real-World Validation
GRACE, a gastric-specific pathology foundation model trained on multicenter HE-stained slides, outperforms pancancer models on 28 tasks and improves pathologist accuracy, speed, and agreement in a reader study while enabling case triaging.
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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.