Chest pathology classification in X-rays using GANs

Medical datasets are often highly imbalanced with overrepresentation of common medical problems and a shortage of data from rare conditions. We propose simulation of pathology in images to overcome the above limitations. Using chest X-rays as a model medical image, we implement a generative adversarial network (GAN) to create artificial images based upon a modest sized labeled dataset. We employ a combination of real and artificial images to train a deep convolutional neural network (DCNN) to detect pathology across fiveclasses (Cardiomegaly, Pleural Effusion ,Pulmonary Edema, Pneumothorax and Normal) of chest X-rays.