Tensor-based Complex-valued Graph Neural Network for Dynamic Coupling Multimodal brain Networks
- Resource Type
- Conference
- Authors
- Yang, Yanwu; Cai, Guoqing; Ye, Chenfei; Xiang, Yang; Ma, Ting
- Source
- ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2023 - 2023 IEEE International Conference on. :1-5 Jun, 2023
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Signal Processing and Analysis
Couplings
Tensors
Signal processing
Brain modeling
Graph neural networks
Arrays
Medical diagnosis
Multimodal
Tensor
Graph neural network
Neuroimage
Gating
- Language
- ISSN
- 2379-190X
The multi-modal neuroimage study has dramatically facilitated disease diagnosis. Tensor-based methods are commonly used to represent multi-modal data as multi-dimensional arrays and usually implement matrix decomposition. These methods can be seen as a linear algebraic way for the lossy compression of an array. However, involved lossy operations might have a negative impact on performance, and overlook underlying important complementary information between modalities. This study proposes a Tensor-based Complex-valued Graph Neural Network (TC-GNN) to model multimodal neuroimages as complex-valued tensor graphs by investigating underlying complementary associations and cross-modality message aggregation. Experiments on two real-world datasets demonstrate our method’s consistent improvements and superiority over other baseline models in multi-modal brain disease analysis.