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ORIGINAL RESEARCH article
Front. Med.
Sec. Precision Medicine
Volume 11 - 2024 |
doi: 10.3389/fmed.2024.1496573
This article is part of the Research Topic High-order Correlation Mining in Medical Applications View all 3 articles
A Hypergraph Transformer Method for Brain Disease Diagnosis
Provisionally accepted- 1 Tsinghua University, Beijing, Beijing, China
- 2 Xi'an Jiaotong University, Xi'an, China
- 3 Shenzhen KangNing Hospital, Shenzhen, Guangdong Province, China
- 4 Shenzhen Mental Health Centre, Shenzhen, Guangdong, China
Objective: To address the high-order correlation modeling and fusion challenges between functional and structural brain networks.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.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.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.
Keywords: Hypergraph Computation, brain network, High-order correlation, brain disease diagnosis, transformer
Received: 14 Sep 2024; Accepted: 30 Oct 2024.
Copyright: © 2024 Han, Feng, Xu, Du and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Junchang Li, Shenzhen KangNing Hospital, Shenzhen, Guangdong Province, China
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