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.

翻译：在生物医学图像分析中，深度学习方法的适用性直接受限于可用图像数据的数量。这是因为深度学习模型需要大规模图像数据集才能实现高性能。生成对抗网络（GANs）被广泛用于通过生成合成生物医学图像来应对数据限制问题。GANs由两个模型构成：生成器——一个基于接收的反馈学习如何生成合成图像的模型；判别器——一个将图像分类为合成或真实并向生成器提供反馈的模型。在整个训练过程中，GANs可能面临若干阻碍生成合适合成图像的技术挑战。第一，模式坍塌问题，即生成器要么生成完全相同的图像，要么从不同输入特征中生成统一的图像。第二，非收敛问题，即梯度下降优化器无法达到纳什均衡。第三，梯度消失问题，即由于判别器达到最优分类性能导致无法向生成器提供有意义的反馈，从而引发不稳定的训练行为。这些问题导致生成的合成图像模糊、不真实且多样性不足。迄今为止，尚无综述性文章阐述这些技术挑战在生物医学图像领域的影响。本研究提出了一篇综述文章，并基于生物医学图像领域中GANs训练问题的解决方案建立了分类体系。本综述重点阐述了关键挑战，并指明了生物医学图像领域中GANs训练的未来研究方向。