Self-supervised ensembled learning for autism spectrum classification

Purpose: Deep learning has made remarkable progress in classifying autism spectrum disorder (ASD) using neuroimaging data. However, the current methods rely mainly on supervised learning, which requires a large amount of manually labeled data, making it an expensive and difficult task to scale.Methods: To overcome this limitation, we propose a novel ensemble-based framework that learns a transferable and generalizable visual representation from different self-supervised features for the downstream task of ASD classification. This framework dynamically learns a superior representation by aggregating complementary information in the frequency domain from independent self-supervised features with limited data. Additionally, to address the information loss caused by the dimensionality reduction of 3D fMRI data, we propose a thresholding algorithm to optimally extract the most discriminant features from 2D rs-fMRI data.Results: Experimental results demonstrate that the proposed method outperforms previous stateof-the-art methods by 19.69% on the ABIDE-1 dataset with a 10-fold cross-validation accuracy of 94.51%.Conclusion: The proposed method learns a transferrable and generalizable ensembled representation by leveraging complementary information encoded in different self-supervised representations for ASD classification.