  Generative adversarial networks have been successfully applied to inpainting
in natural images. However, the current state-of-the-art models have not yet
been widely adopted in the medical imaging domain. In this paper, we
investigate the performance of three recently published deep learning based
inpainting models: context encoders, semantic image inpainting, and the
contextual attention model, applied to chest x-rays, as the chest exam is the
most commonly performed radiological procedure. We train these generative
models on 1.2M 128 $\times$ 128 patches from 60K healthy x-rays, and learn to
predict the center 64 $\times$ 64 region in each patch. We test the models on
both the healthy and abnormal radiographs. We evaluate the results by visual
inspection and comparing the PSNR scores. The outputs of the models are in most
cases highly realistic. We show that the methods have potential to enhance and
detect abnormalities. In addition, we perform a 2AFC observer study and show
that an experienced human observer performs poorly in detecting inpainted
regions, particularly those generated by the contextual attention model.
