Evolution of conditional-GANs for the synthesis of chest x-ray images

Deep learning (DL) is now widely used to perform tasks involving the analysis of biomedical imaging. However, the small amounts available of annotated examples of these types of images make it difficult to use DL-based systems, since large amounts of data are required for adequate generalization and performance. For this reason, in recent years, Generative Adversarial Networks (GANs) have been used to obtain synthetic images that artificially increase the amount available. Despite this, the usual training instability in GANs, in addition to their empirical design, does not always allow for high-quality results. Through the neuroevolution of GANs it has been possible to reduce these problems, but many of these works use benchmark datasets with thousands of images, a scenario that does not reflect the real conditions of cases in which it is necessary to increase the data due to the limited amount available. In this work, cDCGAN-PSO is presented, an algorithm for the neuroevolution of GANs that adapts the concepts of the DCGAN-PSO to a conditional-DCGAN that allows the synthesis of three classes of chest X-ray images and that is trained with only 600 images of each class. The synthetic images obtained from evolved GANs show good similarity with real chest X-ray images.