Before revision:  We demonstrate that using our proposed GAN-based data augmentation method significantly improves the performance of the state-of-the-art anomaly detection architecture, AnoGAN, in detecting pneumonia in chest X-rays, increasing AUC from 0.83 to 0.88 .
After revision:  We demonstrate that using our proposed GAN-based augmentation method surpasses other augmentation methods for training a GAN in detecting anomalies in X-ray images .