ADHD Classification with Robust Biomarker Detection Using Knowledge Distillation

Deep learning methods have been extensively employed in the classification of attention deficit hyperactivity disorder (ADHD) in decades. However, these methods either are still with unsatisfactory accuracy or lack the ability to capture relevant biological markers. To address these challenges, we introduce a knowledge distillation architecture within a binary hypothesis testing framework. In detail, we employ an existing AENet as a teacher model and guide a student model in learning high-level features of ADHD disease. More importantly, an attention block is adopted in this student model to obtain attention weights and better quantify the contributions of used brain functional connections. Now, these weights become robust in coping with the individual diversity among ADHD and healthy control groups, making it convenient for biomarker detection. Experiments demonstrate that our method attains an average classification accuracy of 99.5%. ADHD biomarkers are effectively identified and solidly supported by recent reports. This further illustrates the validity of our knowledge distillation approach.