Improving the level of autism discrimination with augmented data by GraphRNN

Haonan Sun, Qiang He, Shouliang Qi, Yudong Yao, Yueyang Teng

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Datasets are the key to deep learning in autism disease research. However, due to the small quantity and heterogeneity of samples in current public datasets, for example Autism Brain Imaging Data Exchange (ABIDE), the recognition research is not sufficiently effective. Previous studies primarily focused on optimizing feature selection methods and data augmentation to improve recognition accuracy. This research is based on the latter, which learns the edge distribution of a real brain network through the graph recurrent neural network (GraphRNN) and generates synthetic data that have an incentive effect on the discriminant model. Experimental results show that the synthetic data greatly improves the classification ability of the subsequent classifiers, for example, it can improve the classification accuracy of a 50-layer ResNet by up to 30% compared with the case without synthetic data.

Original languageEnglish
Article number106141
JournalComputers in Biology and Medicine
Volume150
DOIs
StatePublished - Nov 2022

Keywords

  • Autism
  • Data augmentation
  • Functional connectivity
  • Graph recurrent neural network (graphRNN)
  • Link prediction

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