  Recent works show that Generative Adversarial Networks (GANs) can be
successfully applied to chest X-ray data augmentation for lung disease
recognition. However, the implausible and distorted pathology features
generated from the less than perfect generator may lead to wrong clinical
decisions. Why not keep the original pathology region? We proposed a novel
approach that allows our generative model to generate high quality plausible
images that contain undistorted pathology areas. The main idea is to design a
training scheme based on an image-to-image translation network to introduce
variations of new lung features around the pathology ground-truth area.
Moreover, our model is able to leverage both annotated disease images and
unannotated healthy lung images for the purpose of generation. We demonstrate
the effectiveness of our model on two tasks: (i) we invite certified
radiologists to assess the quality of the generated synthetic images against
real and other state-of-the-art generative models, and (ii) data augmentation
to improve the performance of disease localisation.
