Overcoming Challenges of Small Data and Over-parameterized DNN in fMRI-based Diagnosis

Classification of High-Dimensional Low Sample Size (HDLSS) datasets is a serious challenge, especially using Deep Neural Networks (DNNs).We present an improved DNN method for the classification of HDLSS dataset with the application of psychiatric and neurological disorders based on rest-fMRI brain signals. According to the comparison of several state-of-the-art supervised vs. self-supervised metric loss functions, we find the best method, i.e., self-supervised Mixup, and suggest some modifications. We propose Triplet Mixup, which locally samples neighbored triplets and augments new data within the triangular space, rather than the linear interpolation between pairs in classic Mixup. The loss is also extended to the Triplet Mixup loss. Our modifications promote better exploration of embedding space thus more diverse augmented data.Experimental results show that our method not only does not overfit despite too small datasets but also achieves almost best classification accuracies in disease predictions. The results also confirm the theory of overparameterized but generalized DNNs in the HDLSS setting on fMRI data.