  In this paper, we present a data augmentation method that generates synthetic
medical images using Generative Adversarial Networks (GANs). We propose a
training scheme that first uses classical data augmentation to enlarge the
training set and then further enlarges the data size and its diversity by
applying GAN techniques for synthetic data augmentation. Our method is
demonstrated on a limited dataset of computed tomography (CT) images of 182
liver lesions (53 cysts, 64 metastases and 65 hemangiomas). The classification
performance using only classic data augmentation yielded 78.6% sensitivity and
88.4% specificity. By adding the synthetic data augmentation the results
significantly increased to 85.7% sensitivity and 92.4% specificity.
