  Medical datasets are often highly imbalanced with over-representation of
common medical problems and a paucity 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 five classes of
chest X-rays. Furthermore, we demonstrate that augmenting the original
imbalanced dataset with GAN generated images improves performance of chest
pathology classification using the proposed DCNN in comparison to the same DCNN
trained with the original dataset alone. This improved performance is largely
attributed to balancing of the dataset using GAN generated images, where image
classes that are lacking in example images are preferentially augmented.
