English(EN) Do Saliency Maps Fail Dense Breasts? A Chance-Normalized, Confounder-Aware Audit of Explanation Fairness in Mammography

乳腺X线摄影分类器中的显著性图谱对致密乳腺存在偏见

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-05 05:00

一篇新发表在arXiv上的研究论文调查了乳腺X线摄影分类器中显著性图谱的公平性，特别关注致密乳腺，因为在致密乳腺中癌症可能更难检测。研究发现，旨在显示分类器在寻找癌症时关注区域的显著性图谱，在致密乳腺中的表现不如在非致密乳腺中。研究人员发现，钙化点的存在以及分类器在致密乳腺中对病灶的依赖性降低等因素导致了这些性能差距。他们建议报告分类器的这些特定于密度的性能差异，并控制病灶的可见性和模型对病灶的依赖性。 AI

影响凸显了AI诊断工具中潜在的偏见，强调了在医疗应用中进行公平性审计的必要性。

排序理由发表在arXiv上的研究论文，详细介绍了对AI模型解释公平性的新审计。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Ahmed Jaheen · 2026-11-05 05:00

显著性图谱对密集型乳房是否失效？一项机会-标准化、混淆因素感知的乳腺X线摄影解释公平性审计

arXiv:2611.05909v1 Announce Type: cross Abstract: Saliency maps, offered as evidence that mammography classifiers use the cancer, are untested in dense breasts, which can hide cancers. We tested whether they locate cancers worse there, and why. On 1,345 malignant CBIS-DDSM views,…

本文如何被排名

报道来源 [1]

显著性图谱对密集型乳房是否失效？一项机会-标准化、混淆因素感知的乳腺X线摄影解释公平性审计

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