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

arXiv: https://arxiv.org/abs/2110.10489 · HTML: https://mlsanity.com/paper/2110.10489/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 38 references that are in the map.
- First posted on arXiv 20 Oct 2021.

## Builds on (most cited first, 4 of 4)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Review](https://mlsanity.com/paper/2007.01285/) (2020)
- [A Hybrid 3DCNN and 3DC-LSTM based model for 4D Spatio-temporal fMRI data: An ABIDE Autism Classification study](https://mlsanity.com/paper/2002.05981/) (2020)
- [What is the best data augmentation for 3D brain tumor segmentation?](https://mlsanity.com/paper/2010.13372/) (2020)

## Built on by (most cited first, 2 of 2)

- [Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review](https://mlsanity.com/paper/2206.11233/) (2022)
- [Autism Spectrum Disorder Classification with Interpretability in Children based on Structural MRI Features Extracted using Contrastive Variational Autoencoder](https://mlsanity.com/paper/2307.00976/) (2023)

## 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.