  In biomedical image analysis, the applicability of deep learning methods is
directly impacted by the quantity of image data available. This is due to deep
learning models requiring large image datasets to provide high-level
performance. Generative Adversarial Networks (GANs) have been widely utilized
to address data limitations through the generation of synthetic biomedical
images. GANs consist of two models. The generator, a model that learns how to
produce synthetic images based on the feedback it receives. The discriminator,
a model that classifies an image as synthetic or real and provides feedback to
the generator. Throughout the training process, a GAN can experience several
technical challenges that impede the generation of suitable synthetic imagery.
First, the mode collapse problem whereby the generator either produces an
identical image or produces a uniform image from distinct input features.
Second, the non-convergence problem whereby the gradient descent optimizer
fails to reach a Nash equilibrium. Thirdly, the vanishing gradient problem
whereby unstable training behavior occurs due to the discriminator achieving
optimal classification performance resulting in no meaningful feedback being
provided to the generator. These problems result in the production of synthetic
imagery that is blurry, unrealistic, and less diverse. To date, there has been
no survey article outlining the impact of these technical challenges in the
context of the biomedical imagery domain. This work presents a review and
taxonomy based on solutions to the training problems of GANs in the biomedical
imaging domain. This survey highlights important challenges and outlines future
research directions about the training of GANs in the domain of biomedical
imagery.
