Researchers have introduced BagShift, a new protocol designed to measure how changes in patch selection affect the evidence observed by whole-slide multiple-instance learning (MIL) models. This method isolates the impact of the selector from variations in the case mix. Experiments on the PANDA and CAMELYON16 datasets revealed that different patch selection strategies, even with identical computational budgets, can expose significantly different evidence to the model, leading to substantial drops in performance metrics like quadratic weighted kappa. The findings suggest that patch count alone does not dictate observed evidence, and deployment evaluations should report both preserved evidence and aggregation methods. AI
IMPACT Highlights the critical role of patch selection in MIL models, suggesting a need for more robust evaluation methods in AI deployment.
RANK_REASON The cluster contains a research paper detailing a new protocol and experimental findings. [lever_c_demoted from research: ic=1 ai=1.0]
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