GENERATION OF ARTIFICIAL CHEST RADIOGRAPHS USING GENERATIVE ADVERSARIAL NEURAL NETWORKS

Abstract

This paper deals with the problem of generating artificial chest x-ray images which expected to be almost undistinguishable from real ones. Generation was performed using Generative Adversarial Nets (GAN). Similarity of resultant artificial images to the real ones was evaluated both by visual examination and by quantitative assessment using commonly known Local Binary Patterns. It was concluded that GANs can be successfully employed for generating realistically appearing artificial chest radiographs. However, an automatic procedure of selecting “most realistic” results is necessary for excluding the final visual quality control stage and making the whole generation process fully automatic.