Deep Feature Extraction for Resting-State Functional MRI by Self-Supervised Learning and Application to Schizophrenia Diagnosis.

ABSTRACT: In this study, we propose a deep-learning technique for functional MRI analysis. We introduced a novel self-supervised learning scheme that is particularly useful for functional MRI wherein the subject identity is used as the teacher signal of a neural network. The neural network is trained solely based on functional MRI-scans, and the training does not require any explicit labels. The proposed method demonstrated that each temporal volume of resting state functional MRI contains enough information to identify the subject. The network learned a feature space in which the features were clustered per subject for the test data as well as for the training data; this is unlike the features extracted by conventional methods including region of interests (ROIs) pooling signals and principal compo

SUBMITTER: Hashimoto Y 

PROVIDER: S-EPMC8429808 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature