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English(EN) BagShift: Measuring How Patch Selection Changes the Evidence Seen by Whole-Slide MIL

BagShift研究量化了补丁选择对全切片MIL模型的影响

作者 PulseAugur 编辑部 · [1 个来源] ·

一项新的研究论文介绍了BagShift，一种衡量补丁选择的变化如何影响全切片多实例学习（MIL）模型所见证据的方法。研究表明，即使在固定的计算预算下，改变补丁选择过程也会导致截然不同的结果。例如，在PANDA数据集上，与广泛采样相比，将补丁选择集中在单个坐标周围会将一个关键性能指标降低17.96个点。该研究强调了在部署过程中评估补丁选择器保留的内容以及如何聚合观测结果的重要性。 AI

影响 强调了对全切片成像模型中补丁选择进行稳健评估的关键需求，影响了诊断AI的开发。

排序理由 研究论文，详细介绍了一种评估机器学习模型的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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BagShift研究量化了补丁选择对全切片MIL模型的影响

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报道来源 [1]

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

    BagShift：衡量补丁选择如何改变全切片MIL所见的证据

    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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