Summary of A Heterogeneous Graph Neural Network Fusing Functional and Structural Connectivity For Mci Diagnosis, by Feiyu Yin et al.
A Heterogeneous Graph Neural Network Fusing Functional and Structural Connectivity for MCI Diagnosis
by Feiyu Yin, Yu Lei, Siyuan Dai, Wenwen Zeng, Guoqing Wu, Liang Zhan, Jinhua Yu
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel dual-modal fusion method is proposed to better leverage the rich heterogeneity in resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) data. The method, based on heterogeneous graph neural networks (HGNNs), integrates functional and structural connectivity by establishing both homo- and hetero-meta-paths. A heterogeneous graph pooling strategy is introduced to balance the two types of paths, preventing feature confusion after pooling. Additionally, a heterogeneous graph data augmentation approach is proposed to address sample imbalance issues commonly seen in clinical diagnosis. The method is evaluated on the ADNI-3 dataset for mild cognitive impairment (MCI) diagnosis and achieves a mean classification accuracy of 93.3%, outperforming other algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to combine brain scan data from two different types of imaging machines, called rs-fMRI and DTI, is developed. This combination helps doctors better understand changes in the brain that happen with certain disorders. The method uses special computer programs called graph neural networks (GNNs) to join together information from both types of scans. It also helps fix a problem where one type of scan might dominate the other, giving a more complete picture of what’s going on in the brain. |
Keywords
» Artificial intelligence » Classification » Data augmentation » Diffusion
