Triplet-Based Deep Hashing Incremental Learning for Brain Network Classification.

Abstract

Due to the limitations of collection conditions and costs, public brain network datasets generally combine data from multiple sites. However, the difference among multi-source data collected from multiple sites always affects classification performance. To overcome the problem, we propose a triplet-based deep hashing incremental learning (Tri-DHIL) method for brain network classification, which learns data streams from each site incrementally rather than from collections of multiple sites. Specifically, the Tri-DHIL method is divided into three phases. In the site queue generation phase, we rank the sites based on their sample quantity and label information. In the triplet-based deep hashing learning phase, we first cluster samples of the site using diagnostic labels and take the clustering center as the anchor point. Then, we choose two samples to form a triplet with the anchor point, one from the same cluster as the anchor point and the other from a different cluster. The construction of triplets can not only enrich the input data of the model but also facilitate the maintenance of similarity relationships in the process of model learning. Finally, we input the triplets into the deep hashing learning model for feature extraction and hash mapping. In the incremental learning phase, we adjust the model parameters by accumulating the triplet-based losses of the previous site, which can prevent the model from forgetting the previously learned features after learning the features of the new site. Experimental results on ABIDE I, ABIDE II, and ADHD-200 demonstrate that the Tri-DHIL method exhibits competitive classification performance.