Feature and Semantic Matching Multisource Domain Adaptation for Diagnostic Classification of Neuropsychiatric Disorders

Multi-site resting-state functional magnetic resonance imaging (rs-fMRI) data have been increasingly utilized for diagnostic classification of neuropsychiatric disorders such as schizophrenia (SCZ) and major depressive disorder (MDD). However, the cross-site generalization ability of deep networks is limited due to the significant inter-site data heterogeneity caused by different MRI scanners or scanning protocols. To address this issue, we propose a feature and semantic matching multi-source domain adaptation method (FSM-MSDA) to learn site-invariant disorder-related feature representations. In FSM-MSDA, we adopt separate feature extractors for multiple source domains, and propose an accurate feature matching module to align the category-level feature distributions across multiple source domains and target domain. In addition, we also propose a semantic feature alignment module to eliminate the distribution discrepancy in high-level semantic features extracted from target samples by different source classifiers. Extensive experiments based on multi-site fMRI data of SCZ and MDD show the superiority and robustness of FSM-MSDA compared to state-of- the-art methods. Besides, FSM-MSDA achieves the average accuracy of 80.8% in the classification of SCZ, meeting the clinically diagnostic accuracy threshold of 80%. Shared discriminative brain regions including the middle temporal gyrus and the cerebellum regions are identified in the diagnostic classification of SCZ and MDD.