用可解释AI分析自闭症脑区，让模型诊断有依据。
From Predictions to Explanations: Explainable AI for Autism Diagnosis and Identification of Critical Brain Regions
- 用跨域迁移学习缓解自闭症数据少问题
- 结合三种XAI技术识别关键脑区
- 结果与已有神经科学发现高度一致
自闭症谱系障碍（ASD）是一种以大脑发育异常为特征的神经发育障碍。当前机器学习中的迁移学习方法在该领域的应用仍有限。本文提出一种双模块计算机辅助诊断框架：第一模块采用跨域迁移学习微调深度学习模型进行ASD分类；第二模块通过显著性映射、梯度加权类激活映射（Grad-CAM）和SHAP分析三种可解释AI技术，解释模型决策并识别关键脑区。实验表明，跨域迁移学习能有效应对ASD研究中的数据稀缺问题。结合三种可解释性方法，该框架揭示了模型诊断逻辑，并定位出与ASD最相关的脑区。结果与现有神经生物学证据高度吻合，验证了方法的临床意义。
原文摘要 · Abstract (English)
Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by atypical brain maturation. However, the adaptation of transfer learning paradigms in machine learning for ASD research remains notably limited. In this study, we propose a computer-aided diagnostic framework with two modules. This chapter presents a two-module framework combining deep learning and explainable AI for ASD diagnosis. The first module leverages a deep learning model fine-tuned through cross-domain transfer learning for ASD classification. The second module focuses on interpreting the model decisions and identifying critical brain regions. To achieve this, we employed three explainable AI (XAI) techniques: saliency mapping, Gradient-weighted Class Activation Mapping, and SHapley Additive exPlanations (SHAP) analysis. This framework demonstrates that cross-domain transfer learning can effectively address data scarcity in ASD research. In addition, by applying three established explainability techniques, the approach reveals how the model makes diagnostic decisions and identifies brain regions most associated with ASD. These findings were compared against established neurobiological evidence, highlighting strong alignment and reinforcing the clinical relevance of the proposed approach.
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