新方法增强医学领域视觉语言模型的可解释性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-28 04:00

研究人员开发了一种名为ParseFIxLIP的新方法，旨在提高视觉语言模型（VLMs）在医学应用中的可解释性。该新方法将树形语法分析（Tree-Gram Parsing）整合到Banzhaf交互博弈中，解决了临床术语中分词器（tokenizers）造成的概念碎片化问题。通过根据依赖分析树将语义相关的文本标记分组为连贯单元，ParseFIxLIP生成了更具可解释性的跨模态归因。该方法已在BiomedCLIP上使用ROCOv2的医学影像进行了验证，证明了其能够捕捉分组词对模型预测的协同影响，并提供临床相关的见解。 AI

影响通过提供更连贯、更具可解释性的解释，提高了医学领域VLMs的可靠性和临床应用性。

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arXiv cs.AI TIER_1 English(EN) · Jakub Rymarski (University of Warsaw, Poland), Adam Rempa{\l}a (University of Warsaw, Poland), Bart{\l}omiej Sobieski (University of Warsaw, Poland), Przemys{\l}aw Biecek (University of Warsaw, Poland) · 2026-07-28 04:00

利用支持树形语法分析的加权Banzhaf交互解释BiomedCLIP

arXiv:2607.23368v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) are demonstrating significant capabilities in medical tasks like radiology analysis, yet providing faithful and interpretable explanations remains a key consideration for their responsible deployment …

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利用支持树形语法分析的加权Banzhaf交互解释BiomedCLIP

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