AI RESEARCH
Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts
arXiv CS.LG
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ArXi:2410.06723v2 Announce Type: replace-cross Pathology foundation models (PFMs) have emerged as powerful pretrained encoders for computational pathology, but their robustness under clinically relevant distribution shifts remains insufficiently understood. We benchmark the robustness of recent PFMs in the setting of prostate cancer grading from whole-slide images (WSIs