A Survey on Training Challenges in Generative Adversarial Networks for Biomedical Image Analysis

Abstract

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.

Computational Intelligence Approaches for Enhancing Biomedical Image Processing Applications Based on Breast Cancer

“…Several research articles related to this exist, like [8], which focus on biomedical signals techniques for the detection of Covid-19, [9], which centered on monitoring systems remotely for personal health during Covid-19 pandemic using biomedical signals, an overview of the diagnosis of Schizophrenia using a medical image by [10], recent advances, challenges, and way forward of medical image analysis utilizing machine learning techniques by [11], generative Adversarial Networks for Biomedical Image Analysis by [12,13] biomedical applications using machine learning techniques. All these articles focus on medical image processing or biomedical signals.…”