改进了AI解释方法，让脑部影像的痴呆诊断更清晰可信。
Path-Weighted Integrated Gradients for Interpretable Dementia Classification
- 引入可调权重函数，动态强化路径中关键区域的贡献
- 在OASIS-1数据集上准确识别出与痴呆阶段相关的脑区
- 适合关注模型可解释性与临床医学结合的研究者
集成梯度（IG）是可解释人工智能中广泛使用的归因方法。本文提出路径加权集成梯度（PWIG），对IG进行推广，将可定制的加权函数引入归因积分。该改进允许针对基线到输入之间的不同路径段进行重点强调，从而提升可解释性、降低噪声干扰，并检测特征相关性随路径变化的情况。我们建立了其理论性质，并在使用OASIS-1 MRI数据集的痴呆分类任务中验证其有效性。PWIG生成的归因图能清晰突出与不同痴呆阶段相关的临床有意义脑区，提供清晰且稳定的解释。结果表明，PWIG是一种灵活且理论完备的方法，可显著提升复杂预测模型的归因质量。
原文摘要 · Abstract (English)
Integrated Gradients (IG) is a widely used attribution method in explainable artificial intelligence (XAI). In this paper, we introduce Path-Weighted Integrated Gradients (PWIG), a generalization of IG that incorporates a customizable weighting function into the attribution integral. This modification allows for targeted emphasis along different segments of the path between a baseline and the input, enabling improved interpretability, noise mitigation, and the detection of path-dependent feature relevance. We establish its theoretical properties and illustrate its utility through experiments on a dementia classification task using the OASIS-1 MRI dataset. Attribution maps generated by PWIG highlight clinically meaningful brain regions associated with various stages of dementia, providing users with sharp and stable explanations. The results suggest that PWIG offers a flexible and theoretically grounded approach for enhancing attribution quality in complex predictive models.
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。