PointExplainer: Towards Transparent Parkinson's Disease Diagnosis
This addresses the problem of clinical trust in AI diagnostics for Parkinson's disease, offering an incremental improvement in explainability.
The paper tackles the lack of interpretability in deep neural networks for Parkinson's disease diagnosis from hand-drawn signals by proposing PointExplainer, which assigns attribution values to hand-drawn segments to explain model decisions, achieving intuitive explanations without performance degradation.
Deep neural networks have shown potential in analyzing digitized hand-drawn signals for early diagnosis of Parkinson's disease. However, the lack of clear interpretability in existing diagnostic methods presents a challenge to clinical trust. In this paper, we propose PointExplainer, an explainable diagnostic strategy to identify hand-drawn regions that drive model diagnosis. Specifically, PointExplainer assigns discrete attribution values to hand-drawn segments, explicitly quantifying their relative contributions to the model's decision. Its key components include: (i) a diagnosis module, which encodes hand-drawn signals into 3D point clouds to represent hand-drawn trajectories, and (ii) an explanation module, which trains an interpretable surrogate model to approximate the local behavior of the black-box diagnostic model. We also introduce consistency measures to further address the issue of faithfulness in explanations. Extensive experiments on two benchmark datasets and a newly constructed dataset show that PointExplainer can provide intuitive explanations with no diagnostic performance degradation. The source code is available at https://github.com/chaoxuewang/PointExplainer.