Deep learning has become a popular tool for medical image analysis, but the limited availability of training data remains a major challenge, particularly in the medical field where data acquisition can be costly and subject to privacy regulations. Data augmentation techniques offer a solution by artificially increasing the number of training samples, but these techniques often produce limited and unconvincing results. To address this issue, a growing number of studies have proposed the use of deep generative models to generate more realistic and diverse data that conform to the true distribution of the data. In this review, we focus on three types of deep generative models for medical image augmentation: variational autoencoders, generative adversarial networks, and diffusion models. We provide an overview of the current state of the art in each of these models and discuss their potential for use in different downstream tasks in medical imaging, including classification, segmentation, and cross-modal translation. We also evaluate the strengths and limitations of each model and suggest directions for future research in this field. Our goal is to provide a comprehensive review about the use of deep generative models for medical image augmentation and to highlight the potential of these models for improving the performance of deep learning algorithms in medical image analysis.

翻译：深度学习已成为医学图像分析中的常用工具，但训练数据有限仍然是一个主要挑战，尤其在医学领域，数据获取成本高昂且受隐私法规约束。数据增强技术通过人工增加训练样本数量提供了解决方案，但这些技术通常产生有限且缺乏说服力的结果。为解决此问题，越来越多的研究提出使用深度生成模型生成更真实、更多样化且符合数据真实分布的数据。本综述聚焦于三类用于医学图像增强的深度生成模型：变分自编码器、生成对抗网络和扩散模型。我们概述了每种模型的最新研究进展，并探讨了它们在医学图像不同下游任务中的应用潜力，包括分类、分割和跨模态转换。我们还评估了每种模型的优势与局限性，并为该领域的未来研究方向提出建议。我们的目标是全面综述深度生成模型在医学图像增强中的应用，并强调这些模型在提升深度学习算法在医学图像分析中性能的潜力。