Learning Sequential Information in Task-based fMRI for Synthetic Data
  Augmentation
Jiyao Wang Nicha C. Dvornek Lawrence H. Staib James S. Duncan
Insufficiency of training data is a persistent issue in medical image
analysis, especially for task-based functional magnetic resonance images (fMRI)
with spatio-temporal imaging data acquired using specific cognitive tasks. In
this paper, we propose an approach for generating synthetic fMRI sequences that
can then be used to create augmented training datasets in downstream learning
tasks. To synthesize high-resolution task-specific fMRI, we adapt the
$\alpha$-GAN structure, leveraging advantages of both GAN and variational
autoencoder models, and propose different alternatives in aggregating temporal
information. The synthetic images are evaluated from multiple perspectives
including visualizations and an autism spectrum disorder (ASD) classification
task. The results show that the synthetic task-based fMRI can provide effective
data augmentation in learning the ASD classification task.