Dynamic Dual-Graph Fusion Convolutional Network for Alzheimer’s Disease Diagnosis
- Resource Type
- Conference
- Authors
- Li, Fanshi; Wang, Zhihui; Guo, Yifan; Liu, Congcong; Zhu, Yanjie; Zhou, Yihang; Li, Jun; Liang, Dong; Wang, Haifeng
- Source
- 2023 IEEE International Conference on Image Processing (ICIP) Image Processing (ICIP), 2023 IEEE International Conference on. :675-679 Oct, 2023
- Subject
- Computing and Processing
Signal Processing and Analysis
Pipelines
Feature extraction
Stability analysis
Convolutional neural networks
Medical diagnosis
Alzheimer's disease
Task analysis
Alzheimer’s disease diagnosis
Dynamic graph convolutional networks
Feature graph learning
- Language
In this paper, a dynamic dual-graph fusion convolutional network is proposed to improve Alzheimer’s disease (AD) diagnosis performance. The following are the paper’s main contributions: (a) propose a novel dynamic Graph Convolutional Network (GCN) architecture, which is an end-to-end pipeline for diagnosis of the AD task; (b) the proposed architecture can dynamically adjust the graph structure for GCN to produce better diagnosis outcomes by learning the optimal underlying latent graph; (c) incorporate feature graph learning and dynamic graph learning, giving those useful features of subjects more weight while decreasing the weights of other noise features. Experiments indicate that our model provides flexibility and stability while achieving excellent classification results in AD diagnosis.