PointExplainer: Towards Transparent Parkinson's Disease Diagnosis
Journal:
arXiv
Published Date:
May 4, 2025
Abstract
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.
