Multi-template Based Auto-Weighted Adaptive Structural Learning for ASD Diagnosis

摘要

Autism spectrum disorder (ASD) is a group of neurodevelopmental disorder and its diagnosis is still a challenging issue. To handle it, we propose a novel multi-template ensemble classification framework for ASD diagnosis. Specifically, based on different templates, we construct multiple functional connectivity brain networks for each subject using resting-state functional magnetic resonance imaging (rs-fMRI) data and extract features representations from these networks. Then, our auto-weighted adaptive structural learning model can learn the shared similarity matrix by an adaptive process while selecting informative features. In addition, our method can automatically allot optimal weight for each template without extra weights and parameters. Further, an ensemble classification strategy is adopted to get the final diagnosis results. Our extensive experiments conducted on the Autism Brain Imaging Data Exchange (ABIDE) database demonstrate that our method can improve ASD diagnosis performance. Additionally, our method can detect the ASD-related biomarkers for further medical analysis.