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BagShift research quantifies impact of patch selection on whole-slide MIL models

By PulseAugur Editorial · [1 sources] ·

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]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

BagShift research quantifies impact of patch selection on whole-slide MIL models

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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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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Ruicheng Yuan, Zhenxuan Zhang, Liwei Hu, Anbang Wang, Haijie Xu, Jiawei Luo, Guang Yang ·

    BagShift: Measuring How Patch Selection Changes the Evidence Seen by Whole-Slide MIL

    arXiv:2608.15970v1 Announce Type: new Abstract: Whole-slide multiple-instance learning (MIL) observes only the patches admitted by its selector. Deployment can alter this selector through compute limits, tissue masking, or regional workflows, even when the patch count is unchange…

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