Figure 2.

Illustration of the three deep generative models that are commonly used for medical image augmentation: (a) generative adversarial networks (GANs), which consist of a generator and a discriminator network trained adversarially to generate realistic data; (b) variational autoencoders (VAEs), which consist of an encoder and a decoder network trained to reconstruct data and learn a compact latent representation; and (c) diffusion models, which consist of a forward and backward flow of information through a series of steps to model the data distribution.