English(EN) Explaining Digital Pathology Models via Clustering Activations

新方法使用激活聚类解释数字病理学AI模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-28 04:00

研究人员开发了一种新颖的基于聚类的技术来解释使用卷积神经网络的数字病理学模型的行为。与关注单个幻灯片预测的传统显着性图方法相比，该方法提供了对模型全局操作的更全面理解。该技术可视化聚类以增强对模型性能的信任，有可能加速其在临床环境中的应用。其效用已在检测前列腺癌的模型上得到证明。 AI

排序理由该集群包含一篇详细介绍新研究方法的学术论文。[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; 该集群包含一篇详细介绍新研究方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.CV TIER_1 English(EN) · Adam Bajger, Jan Obdr\v{z}\'alek, Vojt\v{e}ch K\r{u}r, Rudolf Nenutil, Petr Holub, V\'it Musil, Tom\'a\v{s} Br\'azdil · 2026-05-28 04:00

通过聚类激活解释数字病理学模型

arXiv:2511.14558v2 Announce Type: replace Abstract: We present a clustering-based explainability technique for digital pathology models based on convolutional neural networks. Unlike commonly used methods based on saliency maps, such as occlusion, GradCAM, or relevance propagatio…