Dynamic Dual-Graph Fusion Convolutional Network For Alzheimer's Disease Diagnosis
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
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| _version_ | 1866914990757249024 |
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| author | Li, Fanshi Wang, Zhihui Guo, Yifan Liu, Congcong Zhu, Yanjie Zhou, Yihang Li, Jun Liang, Dong Wang, Haifeng |
| author_facet | Li, Fanshi Wang, Zhihui Guo, Yifan Liu, Congcong Zhu, Yanjie Zhou, Yihang Li, Jun Liang, Dong Wang, Haifeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_15484 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Dynamic Dual-Graph Fusion Convolutional Network For Alzheimer's Disease Diagnosis Li, Fanshi Wang, Zhihui Guo, Yifan Liu, Congcong Zhu, Yanjie Zhou, Yihang Li, Jun Liang, Dong Wang, Haifeng Image and Video Processing Artificial Intelligence Graphics 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. |
| title | Dynamic Dual-Graph Fusion Convolutional Network For Alzheimer's Disease Diagnosis |
| topic | Image and Video Processing Artificial Intelligence Graphics |
| url | https://arxiv.org/abs/2308.15484 |