A new research paper introduces BagShift, a method to measure how changes in patch selection affect the evidence seen by whole-slide multiple-instance learning (MIL) models. The study demonstrates that altering the patch selection process, even with a fixed computational budget, can lead to significantly different outcomes. For instance, on the PANDA dataset, concentrating patch selection around a single coordinate reduced a key performance metric by 17.96 points compared to broader sampling. The research highlights the importance of evaluating both what a patch selector preserves and how observations are aggregated during deployment. AI
IMPACT Highlights the critical need for robust evaluation of patch selection in whole-slide imaging models, impacting diagnostic AI development.
RANK_REASON Research paper detailing a new methodology for evaluating machine learning models. [lever_c_demoted from research: ic=1 ai=1.0]
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