M₂DC: A Meta-Learning Framework for Generalizable Diagnostic Classification of Major Depressive Disorder

• Published in: IEEE transactions on medical imaging Vol. 44; no. 2; pp. 855 - 867
• Main Authors: Su, Jianpo, Wang, Bo, Fan, Zhipeng, Zhang, Yifan, Zeng, Ling-Li, Shen, Hui, Hu, Dewen
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2025
• Subjects: Adaptation models; Algorithms; Brain - diagnostic imaging; Brain modeling; Data models; Depressive Disorder, Major - classification; Depressive Disorder, Major - diagnostic imaging; Diagnostic classification; Diseases; distance constraint; graph isomorphism network; Humans; Image Interpretation, Computer-Assisted - methods; Machine Learning; Magnetic Resonance Imaging - methods; meta batch normalization; meta-learning; Metalearning; Neuroimaging - methods; Spatiotemporal phenomena; Training

Psychiatric diseases are bringing heavy burdens for both individual health and social stability. The accurate and timely diagnosis of the diseases is essential for effective treatment and intervention. Thanks to the rapid development of brain imaging technology and machine learning algorithms, diagn...