Cost-Sensitive Weighted Contrastive Learning Based on Graph Convolutional Networks for Imbalanced Alzheimer's Disease Staging

• Published in: IEEE transactions on medical imaging Vol. 43; no. 9; pp. 3126 - 3136
• Main Authors: Hu, Yan, Wang, Jun, Zhu, Hao, Li, Juncheng, Shi, Jun
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2024
• Subjects: Aged; Aged, 80 and over; Algorithms; Alzheimer Disease - diagnostic imaging; Alzheimer’s disease Staging; Brain - diagnostic imaging; cost-sensitive weighted contrastive learning; Feature extraction; Female; Humans; Image Interpretation, Computer-Assisted - methods; imbalanced classification; Machine Learning; Magnetic Resonance Imaging - methods; Male; Neural Networks, Computer; Optical fibers; Prototypes; rs-fMRI; Self-supervised learning; Training; Training data; Vectors

Identifying the progression stages of Alzheimer's disease (AD) can be considered as an imbalanced multi-class classification problem in machine learning. It is challenging due to the class imbalance issue and the heterogeneity of the disease. Recently, graph convolutional networks (GCNs) have b...