Federated Multi-Task Learning for Joint Diagnosis of Multiple Mental Disorders on MRI Scans

• Published in: IEEE transactions on biomedical engineering Vol. 70; no. 4; pp. 1 - 12
• Main Authors: Huang, Zhi-An, Hu, Yao, Liu, Rui, Xue, Xiaoming, Zhu, Zexuan, Song, Linqi, Tan, Kay Chen
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Attention deficit hyperactivity disorder; Autism; autism spectrum disorder; Autism Spectrum Disorder - diagnostic imaging; Biomarkers; Data models; Datasets; Deep learning; Diagnosis; domain shift; Feature extraction; Federated multi-task learning; Functional magnetic resonance imaging; Humans; joint diagnosis; Machine learning; Magnetic Resonance Imaging; Medical diagnostic imaging; Medical imaging; Mental disorders; Mental Disorders - diagnostic imaging; Multitasking; Optimization; Phenotypes; Privacy; Schizophrenia; Task analysis; Training
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2022.3210940

Objective: Deep learning (DL) techniques have been introduced to assist doctors in the interpretation of medical images by detecting image-derived phenotype abnormality. Yet the privacy-preserving policy of medical images disables the effective training of DL model using sufficiently large datasets. As a decentralized computing paradigm to address this issue, federated learning (FL) allows the training process to occur in individual institutions with local datasets, and then aggregates the resultant weights without risk of privacy leakage. Methods: We propose an effective federated multi-task learning (MTL) framework to jointly identify multiple related mental disorders based on functional magnetic resonance imaging data. A federated contrastive learning-based feature extractor is developed to extract high-level features across client models. To ease the optimization conflicts of updating shared parameters in MTL, we present a federated multi-gate mixture of expert classifier for the joint classification. The proposed framework also provides practical modules, including personalized model learning, privacy protection, and federated biomarker interpretation. Results: On real-world datasets, the proposed framework achieves robust diagnosis accuracies of 69.48<inline-formula><tex-math notation="LaTeX">\pm</tex-math></inline-formula>1.6%, 71.44<inline-formula><tex-math notation="LaTeX">\pm</tex-math></inline-formula>3.2%, and 83.29<inline-formula><tex-math notation="LaTeX">\pm</tex-math></inline-formula>3.2% in autism spectrum disorder, attention deficit/hyperactivity disorder, and schizophrenia, respectively. Conclusion: The proposed framework can effectively ease the domain shift between clients via federated MTL. Significance: The current work provides insights into exploiting the advantageous knowledge shared in related mental disorders for improving the generalization capability of computer-aided detection approaches.