mlsanityData to 7 Oct 2026

Evaluation of augmentation methods in classifying autism spectrum disorders from fMRI data with 3D convolutional neural networks

arXiv 2110.10489 ↗First posted 20 Oct 2021eess.IV cs.CV q-bio.NCcited by 2

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Abstract (arXiv)

Classifying subjects as healthy or diseased using neuroimaging data has gained a lot of attention during the last 10 years. Here we apply deep learning to derivatives from resting state fMRI data, and investigate how different 3D augmentation techniques affect the test accuracy. Specifically, we use resting state derivatives from 1,112 subjects in ABIDE preprocessed to train a 3D convolutional neural network (CNN) to perform the classification. Our results show that augmentation only provide minor improvements to the test accuracy.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 4 of its 38 references that are in the map. First posted on arXiv 20 Oct 2021.

Builds on

4 of 38 references.

References · 4

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2020Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Reviewcited by 26
  3. 2020A Hybrid 3DCNN and 3DC-LSTM based model for 4D Spatio-temporal fMRI data: An ABIDE Autism Classification studycited by 6
  4. 2020What is the best data augmentation for 3D brain tumor segmentation?cited by 2

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Cited by year

Citing papers per year: 2022: 1; 2023: 1.

2 papers.

  1. 2022Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A reviewcited by 8
  2. 2023Autism Spectrum Disorder Classification with Interpretability in Children based on Structural MRI Features Extracted using Contrastive Variational Autoencodercited by 0