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English(EN) Region-aware error triage for medical image segmentation with groupwise conformal risk control

新框架改进医学图像分割中的误差检测

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了RACE-Triage，一个旨在提高医学图像分割误差检测准确性的新颖框架。该事后系统分析预测的分割掩码，以识别可能包含错误的区域，而不会改变原始掩码。通过将共形风险控制分别应用于边界和非边界区域，RACE-Triage旨在更有效地标记漏报的错误，特别是那些远离预测轮廓的错误。在ISIC-2018和BUSI等数据集上的实验表明，与标准的汇集控制方法相比，这种区域方法显著减少了漏报的非边界错误比例，同时仅略微增加了被标记为潜在错误的区域。 AI

影响 通过改进误差定位来增强AI驱动的医学图像分析的可靠性。

排序理由 该集群包含一篇详细介绍医学图像分析新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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新框架改进医学图像分割中的误差检测

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该集群包含一篇详细介绍医学图像分析新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Nishi Kanta Paul, Md Shihabul Islam Shovo, Hoileong Lee ·

    具有组间一致风险控制的医学图像分割的区域感知错误分类

    arXiv:2611.08962v1 Announce Type: new Abstract: Segmentation masks are increasingly reviewed rather than redrawn, which requires knowing where a mask is likely wrong. Learned error maps rank pixels by risk, but the number of pixels to warn is chosen ad hoc, and calibrating one wa…

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