  Disease-aware image editing by means of generative adversarial networks
(GANs) constitutes a promising avenue for advancing the use of AI in the
healthcare sector. Here, we present a proof of concept of this idea. While
GAN-based techniques have been successful in generating and manipulating
natural images, their application to the medical domain, however, is still in
its infancy. Working with the CheXpert data set, we show that StyleGAN can be
trained to generate realistic chest X-rays. Inspired by the Cyclic Reverse
Generator (CRG) framework, we train an encoder that allows for faithfully
inverting the generator on synthetic X-rays and provides organ-level
reconstructions of real ones. Employing a guided manipulation of latent codes,
we confer the medical condition of cardiomegaly (increased heart size) onto
real X-rays from healthy patients. This work was presented in the Medical
Imaging meets Neurips Workshop 2020, which was held as part of the 34th
Conference on Neural Information Processing Systems (NeurIPS 2020) in
Vancouver, Canada

Image and Video Processing;Computer Vision and Pattern Recognition