TY - JOUR
T1 - Improving the level of autism discrimination with augmented data by GraphRNN
AU - Sun, Haonan
AU - He, Qiang
AU - Qi, Shouliang
AU - Yao, Yudong
AU - Teng, Yueyang
N1 - Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/11
Y1 - 2022/11
N2 - 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.
AB - 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.
KW - Autism
KW - Data augmentation
KW - Functional connectivity
KW - Graph recurrent neural network (graphRNN)
KW - Link prediction
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U2 - 10.1016/j.compbiomed.2022.106141
DO - 10.1016/j.compbiomed.2022.106141
M3 - Article
C2 - 36191394
AN - SCOPUS:85139081860
SN - 0010-4825
VL - 150
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
M1 - 106141
ER -