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

arXiv: https://arxiv.org/abs/2308.15484 · HTML: https://mlsanity.com/paper/2308.15484/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 5 Aug 2023.

## Builds on (most cited first, 7 of 7)

- [Recurrent Neural Network Regularization](https://mlsanity.com/paper/1409.2329/) (2014)
- [Differentiable Graph Module (DGM) for Graph Convolutional Networks](https://mlsanity.com/paper/2002.04999/) (2020)
- [Spectral Graph Convolutions for Population-based Disease Prediction](https://mlsanity.com/paper/1703.03020/) (2017)
- [Latent-Graph Learning for Disease Prediction](https://mlsanity.com/paper/2003.13620/) (2020)
- [Infinite Feature Selection: A Graph-based Feature Filtering Approach](https://mlsanity.com/paper/2006.08184/) (2020)
- [Multi-modal Disease Classification in Incomplete Datasets Using Geometric Matrix Completion](https://mlsanity.com/paper/1803.11550/) (2018)
- [Joint Prediction and Time Estimation of COVID-19 Developing Severe Symptoms using Chest CT Scan](https://mlsanity.com/paper/2005.03405/) (2020)

## Abstract (arXiv)

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.