Handling Incomplete Multimodal Data in ASD Diagnosis: A Masked Autoencoder Approach With Sex-Stratified Fusion.
Authors
Affiliations (3)
Affiliations (3)
- Department of Computer Science & Engineering, Metropolitan University, Sylhet, Bangladesh.
- Department of Computer Science & Engineering, Islamic University of Technology, Gazipur, Bangladesh.
- Department of Computer Science, University of California, Irvine, California, USA.
Abstract
Autism spectrum disorder (ASD) diagnosis remains challenging due to heterogeneous phenotypic presentations and the inherent complexity of neurodevelopmental patterns. While multimodal approaches combining functional magnetic resonance imaging (fMRI) and behavioral data have shown promise, existing methods suffer from three critical limitations: (1) inability to handle missing modalities in clinical settings, (2) lack of interpretability for clinical decision support, and (3) failure to account for sex-based neurobiological differences. We propose a novel Missing-Modality-Robust Multimodal Fusion Framework (MMR-MF<sup>3</sup>) that addresses these challenges through three key innovations. First, we introduce a masked autoencoder (MAE)-based imputation module that reconstructs missing modalities during inference, enabling robust prediction even with incomplete data. Second, we design a transformer-based cross-modal attention mechanism with modality-type embeddings that captures complex interactions between brain connectivity and behavioral phenotypes. Third, we implement sex-stratified classification heads that account for neurobiological sex differences, improving diagnostic accuracy across demographic groups. Our framework achieves state-of-the-art performance on Autism Brain Imaging Data Exchange (ABIDE) I and II datasets, with AUC of 0.89 for males and 0.86 for females. Critically, we validate our approach using leave-one-site-out (LOSO) cross-validation to ensure generalizability across the highly heterogeneous multisite ABIDE data. Extensive ablation studies demonstrate the contribution of each component, while SHapley Additive exPlanations (SHAP)-based explainability analysis reveals clinically meaningful biomarkers. The proposed framework represents a significant advancement toward clinically deployable ASD diagnostic tools.