Abstract
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.
Abstract (translated)
自闭症谱系障碍（ASD）是一种神经发育状况，其特征是大脑成熟过程中的非典型发展。然而，在机器学习领域中适应迁移学习范式以应用于自闭症研究仍然非常有限。本研究提出了一种计算机辅助诊断框架，包括两个模块。本章介绍了一个结合深度学习和可解释人工智能的双模块框架用于ASD诊断。第一个模块利用通过跨域迁移学习微调过的深度学习模型来进行自闭症分类。第二个模块则侧重于对模型决策进行解读并识别关键脑区。为此，我们采用了三种可解释性AI（XAI）技术：敏感度映射、梯度加权类激活映射和SHapley Additive exPlanations (SHAP) 分析。该框架表明跨域迁移学习可以有效解决自闭症研究中的数据稀缺问题。此外，通过应用三种已建立的可解释性技术，此方法揭示了模型如何做出诊断决策，并识别与ASD最相关的脑区。这些发现与公认的神经生物学证据进行了比较，显示出高度的一致性，并强调了所提出方法的临床相关性。
URL
https://arxiv.org/abs/2509.10523
