Discrepancy Minimization Improves Cross-Hospital Robustness in Digital Pathology 事件

Discrepancy Minimization Improves Cross-Hospital Robustness in Digital Pathology arXiv:2605.25175v1 Announce Type: new Abstract: Pathology foundation models (PFMs) have advanced rapidly in recent years and support training classifiers for a range of histopathology tasks. However, their robustness across hospitals remains limited: performance often degrades when training a classifier on data from one hospital and evaluating it on another target hospital. We address this challenge by fine-tuning