A Method for Analyzing Schizophrenia Based on Graph Encoding and Few-Shot Learning
In research on functional brain diseases based on graph neural networks,the constructed brain networks remain static and typically require a large amount of data for modeling and training.To address these two problems,this paper proposes an analysis and diagnostic model based on graph encoding and Few-shot learning.The model utilizes Pearson correlation coefficient and Self-Attention mechanism to construct an adaptive brain network,and takes temporal features,frequency domain features,and brain network features as inputs to a graph convolutional neural network,thereby dynamically learning the adaptive brain network and graph encoding features.The graph encoding features are used as inputs to a graph prototype network for Few-shot learning and classification.Applying this model to the classification and diagnosis of schizophrenia,experimental results demonstrate an accuracy rate of 83.4%in schizophrenia identification.This provides a novel perspective and approach for brain network research,and opens up new directions for the application of Few-shot learning in schizophrenia studies.
国家科技期刊平台
NSTL
万方数据
维普
