用可解释模型检测语音障碍，让AI诊断更透明可信。
A Concept-based approach to Voice Disorder Detection
- 基于概念的可解释模型，将语音特征与医学概念关联
- 在真实数据集上达到传统深度学习的诊断准确率
- 适合临床医生需要理解AI决策的医疗场景
语音障碍影响大量人群，采用自动化、非侵入式技术进行诊断可显著提升医疗水平，改善患者生活质量。近年来研究表明，深度神经网络（DNN）能有效完成该任务，但其决策过程复杂且不透明，限制了在临床环境中的信任度。本文探索一种基于可解释人工智能（XAI）的替代方法，重点研究概念瓶颈模型（CBM）和概念嵌入模型（CEM），这些模型通过将输入特征与可解释的医学概念关联，实现与传统深度学习方法相当的性能，同时提供更透明、可理解的决策框架，有助于提升AI在医疗诊断中的可信度与应用潜力。
原文摘要 · Abstract (English)
Voice disorders affect a significant portion of the population, and the ability to diagnose them using automated, non-invasive techniques would represent a substantial advancement in healthcare, improving the quality of life of patients. Recent studies have demonstrated that artificial intelligence models, particularly Deep Neural Networks (DNNs), can effectively address this task. However, due to their complexity, the decision-making process of such models often remain opaque, limiting their trustworthiness in clinical contexts. This paper investigates an alternative approach based on Explainable AI (XAI), a field that aims to improve the interpretability of DNNs by providing different forms of explanations. Specifically, this works focuses on concept-based models such as Concept Bottleneck Model (CBM) and Concept Embedding Model (CEM) and how they can achieve performance comparable to traditional deep learning methods, while offering a more transparent and interpretable decision framework.
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