English(EN) Less Annotation, More Interpretation: Prior-Guided Concept Bottleneck Models for Interpretable Cancer Imaging Diagnosis

新的CBM方法减少了可解释癌症影像的标注负担

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-14 04:00

研究人员开发了一种新的方法，使用概念瓶颈模型（CBM）进行可解释的癌症影像诊断。该方法整合了有限的概念标注、类别条件分布匹配和先验初始化，以提高透明度。混合CBM在概念AUC方面显示出显著的提升，尤其是在低标注场景（0-20%）下，同时保持了与黑盒模型相当的诊断性能。 AI

影响这项研究可以显著降低在医学诊断中开发可解释AI模型所需的标注要求。

排序理由该集群包含一篇学术论文，详细介绍了医学影像中可解释AI的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CV TIER_1 English(EN) · Baoqiang Ma, Kenneth Gilhuijs · 2026-08-14 04:00

减少标注，增加解读：先验引导的概念瓶颈模型用于可解释的癌症影像诊断

arXiv:2608.13148v1 Announce Type: new Abstract: Concept bottleneck models (CBMs) can improve the transparency of cancer image diagnostic prediction by expressing predictions through radiological concepts. However, their dependence on instance-level concept annotations limits prac…