Cross-attention-guided subject-adaptive graph learning for multimodal autism classification: integrating structural and functional MRI data
This work proposes a cross-attention-guided subject-adaptive graph network (CAS-GNN) model that integrates structural MRI and resting-state functional connectivity data, effectively fusing complementary multimodal information and provides a promising tool for interpretable and robust ASD diagnosis, accelerating biomarker discovery and development.