Multi-channel Sparse Graph Transformer Network for Early Alzheimer’s Disease Identification
- Resource Type
- Conference
- Authors
- Qiu, Yali; Yu, Shuangzhi; Zhou, Yanhong; Liu, Dongdong; Song, Xuegang; Wang, Tianfu; Lei, Baiying
- Source
- 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) Biomedical Imaging (ISBI), 2021 IEEE 18th International Symposium on. :1794-1797 Apr, 2021
- Subject
- Bioengineering
Computing and Processing
Photonics and Electrooptics
Signal Processing and Analysis
Neuroimaging
Neurology
Fuses
Sociology
Functional magnetic resonance imaging
Alzheimer's disease
Statistics
Multi-modal information fusion
Early Alzheimer’s disease
Multi-channel sparse transformer network
- Language
- ISSN
- 1945-8452
With the aging of the global population and increase in life expectancy, the prevalence, incidence and mortality of Alzheimer’s disease (AD) have increased rapidly. Clinical intervention via early diagnosis can delay the AD progression and improve its prognosis. In this paper, we design a novel multi-channel sparse graph transformer network of automatic early AD identification. The proposed method fuses each subject’s non-image information and image information from the functional magnetic resonance imaging and diffusion tensor imaging. The fused information via local weighted clustering coefficients can be used as the input of the multichannel sparse graph transformation network for early AD identification. Our proposed method achieves promising identification performance on the public Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset.