mlsanityData to 7 Oct 2026

Improving the Level of Autism Discrimination through GraphRNN Link Prediction

arXiv 2202.09538 ↗First posted 19 Feb 2022cs.LG cs.NEcited by 0

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Abstract (arXiv)

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.

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References · 22

  1. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
  2. 2016Variational Graph Auto-Encoderscited by 1,373
  3. 2018Relational inductive biases, deep learning, and graph networkscited by 1,341
  4. 2018GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoderscited by 375
  5. 2020Data Augmentation for Graph Neural Networkscited by 200
  6. 2019Learning Data Augmentation Strategies for Object Detectioncited by 139
  7. 2019Data augmentation using learned transformations for one-shot medical image segmentationcited by 138
  8. 2017GraphGAN: Graph Representation Learning with Generative Adversarial Netscited by 121
  9. 2019Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Datacited by 103
  10. 2017Spectral Graph Convolutions for Population-based Disease Predictioncited by 42
  11. 2019InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Predictioncited by 39
  12. 2020Deep Reinforcement Learning and its Neuroscientific Implicationscited by 33
10 more
  1. 2016Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based examplecited by 31
  2. 2019ASD-DiagNet: A hybrid learning approach for detection of Autism Spectrum Disorder using fMRI datacited by 27
  3. 2020A Survey on Deep Learning for Neuroimaging-based Brain Disorder Analysiscited by 27
  4. 2017Bootstrapping Graph Convolutional Neural Networks for Autism Spectrum Disorder Classificationcited by 20
  5. 2016Modeling the dynamics of human brain activity with recurrent neural networkscited by 12
  6. 2019A Review on Neural Network Models of Schizophrenia and Autism Spectrum Disordercited by 8
  7. 20183D Convolutional Neural Networks for Classification of Functional Connectomescited by 6
  8. 2015Generative models of the human connectomecited by 5
  9. 2017Bridging the Gap between Probabilistic and Deterministic Models: A Simulation Study on a Variational Bayes Predictive Coding Recurrent Neural Network Modelcited by 3
  10. 2017Functional connectivity patterns of autism spectrum disorder identified by deep feature learningcited by 2

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