对比多种可解释性方法，发现语音诊断帕金森病的模型解释仍难助医生决策。
Investigating the Effectiveness of Explainability Methods in Parkinson's Detection from Speech
- 用主流可解释技术生成语音特征重要性图
- 解释结果与模型一致但对医生无实际帮助
- 适合关注模型可信度与临床落地的研究者
帕金森病（PD）患者的语音障碍是早期诊断的重要指标。尽管基于语音的PD检测模型表现优异，其可解释性仍缺乏深入研究。本研究系统评估多种可解释性方法，以识别与帕金森病相关的语音特征，旨在推动高精度、可解释模型在临床诊断与监测中的应用。方法包括：(i) 使用主流可解释技术获取特征归因与显著性图；(ii) 通过多种成熟指标定量评估这些图及其并集、交集的忠实性；(iii) 通过辅助分类器评估显著性图传递的信息量。结果显示，尽管解释结果与分类器一致，但往往无法为领域专家提供有效信息。
原文摘要 · Abstract (English)
Speech impairments in Parkinson's disease (PD) provide significant early indicators for diagnosis. While models for speech-based PD detection have shown strong performance, their interpretability remains underexplored. This study systematically evaluates several explainability methods to identify PD-specific speech features, aiming to support the development of accurate, interpretable models for clinical decision-making in PD diagnosis and monitoring. Our methodology involves (i) obtaining attributions and saliency maps using mainstream interpretability techniques, (ii) quantitatively evaluating the faithfulness of these maps and their combinations obtained via union and intersection through a range of established metrics, and (iii) assessing the information conveyed by the saliency maps for PD detection from an auxiliary classifier. Our results reveal that, while explanations are aligned with the classifier, they often fail to provide valuable information for domain experts.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。