Evaluation of augmentation methods in classifying autism spectrum disorders from fMRI data with 3D convolutional neural networks
This work addresses classification of autism from neuroimaging data, but it is incremental as it shows limited gains from augmentation.
The study tackled classifying autism spectrum disorders from fMRI data using a 3D CNN and evaluated augmentation techniques, finding that augmentation provided only minor improvements to test accuracy.
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.