Systems and methods for simulating medical images using generative adversarial networks

Abstract

One disclosed example method for simulated medical images using GANs includes receiving, by a generative adversarial network ('GAN'), a plurality of training images, the training images associated with an affliction and depicting different stages of progression for the affliction; generating, using the GAN, a latent space based on the training images, the latent space comprising a first set of data points indicating parameters associated with the training images; generating, using the GAN, a second set of data points in the latent space based on simulated images generated from the latent space, the simulated images based at least in part on the first set of data points; after generating the second set of data points: receiving a request for a first simulated image associated with the affliction; and generating and outputting, by the GAN, the first simulated image based on the latent space.