Graph Learning-Based Methods for Generating Missing Brain Networks and Multi-modal Fusion Diagnosis
The multi-modal brain network,which integrates the brain structural and functional networks,can effectively extract the complementary information from different modalities,significantly improving the diagnostic accuracy of neurological diseases such as epilepsy.However,due to the long acquisition time and high acquisition cost of multi-modal data collection,it often faces the problem of modality missingness in practical applications,leading to decreased diagnostic accuracy and generalization ability of the model.To address the issue of modality data completely missing,we propose a method based on graph learning methods and cycle-consistent generative adversarial networks,named Graph-CycleGAN method.This method captures feature information between different brain regions in the brain network by introducing graph neural networks,such as graph convolutional neural networks and graph attention mechanisms.Besides,it strengthens the feature extraction ability of the generative framework and realizes the mutual generation of brain structural network and functional network.In addition,to address the lack of diagnostic result-based evaluations for the quality of generated data,this paper proposes a classification model that integrates real and generated brain networks.Experimental results on the epilepsy dataset indicate that the proposed Graph-CycleGAN method can effectively realize the generation of missing brain network by utilizing the existing modality information.
万方数据
维普
