From Predictions to Explanations: Explainable AI for Autism Diagnosis and Identification of Critical Brain Regions

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 computeraided
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 finetuned
through cross-domain transfer learning for ASD classification. The
second module focuses on interpreting the model’s 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.