English(EN) Learning Label-Efficient Interpretable Medical Image Diagnosis via Semi-supervised Hypergraph Concept Bottleneck Model

新框架提升可解释医学影像诊断能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-02 04:00

研究人员开发了一种新的半监督医学影像诊断框架，提高了可解释性和效率。该方法利用双层超图学习来模拟临床概念之间的复杂关系，并生成领域自适应的伪标签。在胎盘植入谱系、乳腺超声和皮肤病数据集上的实验证明了该框架在提高诊断准确性和为临床医生提供透明决策方面的有效性。 AI

影响引入了一种更具可解释性和标签效率的医学影像诊断方法，有望增加临床医生对AI工具的信任和采用。

排序理由详细介绍新颖方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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本文如何被排名

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Newsworthiness bucket: Tool; 详细介绍新颖方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.CV TIER_1 English(EN) · Yijun Yang, Ruiqiang Xiao, Lijie Hu, Angelica I Aviles-Rivero, Yunzhu Wu, Jing Qin, Lei Zhu · 2026-06-02 04:00

通过半监督超图概念瓶颈模型实现标签高效可解释的医学影像诊断学习

arXiv:2606.01698v1 Announce Type: new Abstract: Deep learning has revolutionized medical image analysis, delivering exceptional diagnostic accuracy across diverse applications. Yet, the lack of interpretability in its decision-making hinders clinical adoption, particularly in hig…