Generative Adversarial Networks as an Advanced Data Augmentation Technique for MRI Data

This paper presents a new methodology for data augmentation through the use of Generative Adversarial Networks. Traditional augmentation strategies are severely limited, especially in tasks where the images follow strict standards, as is the case in medical datasets. Experiments conducted on the ADNI dataset prove that augmentation through GANs outperforms traditional methods by a large margin, based both on the validation accuracy and the models' generalization capability on a holdout test set. Although traditional data augmentation did not seem to aid the classification process in any way, by adding GAN-based augmentation an increase of 11.68% in accuracy was achieved. Furthermore, by combining traditional with GAN-based augmentation schemes, even higher accuracies can be reached.