Improving the Level of Autism Discrimination through GraphRNN Link Prediction

TL;DR

This paper enhances autism classification accuracy by using GraphRNN to generate synthetic brain network data, which, when combined with real data, significantly improves neural network discrimination performance.

Contribution

It introduces a novel application of GraphRNN for generating synthetic brain network data to improve autism recognition accuracy.

Findings

Synthetic data improves ResNet accuracy by 32.51%.

GraphRNN effectively learns edge distribution of brain networks.

Combining real and synthetic data enhances disease understanding.

Abstract

Dataset is the key of deep learning in Autism disease research. However, due to the few quantity and heterogeneity of samples in current dataset, for example ABIDE (Autism Brain Imaging Data Exchange), the recognition research is not effective enough. Previous studies mostly focused on optimizing feature selection methods and data reinforcement to improve accuracy. This paper is based on the latter technique, which learns the edge distribution of real brain network through GraphRNN, and generates the synthetic data which has incentive effect on the discriminant model. The experimental results show that the combination of original and synthetic data greatly improves the discrimination of the neural network. For instance, the most significant effect is the 50-layer ResNet, and the best generation model is GraphRNN, which improves the accuracy by 32.51% compared with the model reference experiment without generation data reinforcement. Because the generated data comes from the learned edge connection distribution of Autism patients and typical controls functional connectivity, but it has better effect than the original data, which has constructive significance for further understanding of disease mechanism and development.