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

2021/10/20 by Johan Jönemo, Jönemo, Johan, David Abramian +3
Engineering · Neuroscience · #Advanced Memory and Neural Computing #Computer Vision and Pattern Recognition (cs.CV) #EEG and Brain-Computer Interfaces #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Electrical engineering #Functional Brain Connectivity Studies #Image and Video Processing (eess.IV) #Neurons and Cognition (q-bio.NC) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2110.10489

openalex publication_date 2021/10/20 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28

Abstract

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.

Related

• Deep reinforcement learning for fMRI prediction of Autism Spectrum Disorder
• Explainable AI for Autism Diagnosis: Identifying Critical Brain Regions Using fMRI Data
• Brain Biomarker Interpretation in ASD Using Deep Learning and fMRI
• Denoising VAE as an Explainable Feature Reduction and Diagnostic Pipeline for Autism Based on Resting state fMRI
• Identification of Novel Diagnostic Neuroimaging Biomarkers for Autism Spectrum Disorder Through Convolutional Neural Network-Based Analysis of Functional, Structural, and Diffusion Tensor Imaging Data Towards Enhanced Autism Diagnosis