Dynamic Dual-Graph Fusion Convolutional Network For Alzheimer's Disease Diagnosis

TL;DR

This paper introduces a dynamic dual-graph fusion convolutional network that adaptively learns optimal graph structures to enhance Alzheimer's disease diagnosis accuracy, offering a flexible and stable end-to-end solution.

Contribution

It presents a novel dynamic GCN architecture that adjusts graph structures during training and integrates feature and dynamic graph learning for improved diagnosis.

Findings

Achieved high classification accuracy in AD diagnosis

Demonstrated the model's flexibility and stability

Outperformed existing methods in experimental results

Abstract

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 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.