ADHD Classification with Biomarker Identification Using a Triplet Loss Attention Auto-Encoding Network

Abstract

Deep learning methods have been widely applied in Attention Deficit Hyperactivity Disorder (ADHD) classification in the past decade due to their effective learned features. However, these features are lack of neurobiological meanings and hard to be biomarkers. Here, we proposed an attention auto-encoding network with triplet loss (Tri-Att-AENet) for both ADHD classification and biomarker identification. Taking brain functional connectivities (FCs) as material, we introduced an attention encoding subnetwork to obtain the weighted FCs with their weights as attention scores. A triplet loss function was further employed on these scores, providing sufficient evidence for biomarker selection. Meanwhile, the weighted FCs became discriminative to pursue a higher accuracy. Experiments show that our method achieves an average accuracy of 99.6% for classification. The selected FC biomarkers are in accord with reported neurobiological results and well fulfill the task of biomarker identification.