A hypergraph transformer method for brain disease diagnosis
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Published on: June 26, 2013
Xiangmin Han1, Jingxi Feng2, Heming Xu2
1School of Software, Tsinghua University, Beijing, China.
Objective:
To address the high-order correlation modeling and fusion challenges between functional and structural brain networks.
Method:
This paper proposes a hypergraph transformer method for modeling high-order correlations between functional and structural brain networks. By utilizing hypergraphs, we can effectively capture the high-order correlations within brain networks. The Transformer model provides robust feature extraction and integration capabilities that are capable of handling complex multimodal brain imaging.
Results:
The proposed method is evaluated on the ABIDE and ADNI datasets. It outperforms all the comparison methods, including traditional and graph-based methods, in diagnosing different types of brain diseases. The experimental results demonstrate its potential and application prospects in clinical practice.
Conclusion:
The proposed method provides new tools and insights for brain disease diagnosis, improving accuracy and aiding in understanding complex brain network relationships, thus laying a foundation for future brain science research.
