College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China.
School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran; Department of Computer Science, School of Computing, National University of Singapore, Singapore, Singapore.
Comput Biol Med. 2022 May;144:105382. doi: 10.1016/j.compbiomed.2022.105382. Epub 2022 Mar 5.
With the development of deep learning, the number of training samples for medical image-based diagnosis and treatment models is increasing. Generative Adversarial Networks (GANs) have attracted attention in medical image processing due to their excellent image generation capabilities and have been widely used in data augmentation. In this paper, a comprehensive and systematic review and analysis of medical image augmentation work are carried out, and its research status and development prospects are reviewed.
This paper reviews 105 medical image augmentation related papers, which mainly collected by ELSEVIER, IEEE Xplore, and Springer from 2018 to 2021. We counted these papers according to the parts of the organs corresponding to the images, and sorted out the medical image datasets that appeared in them, the loss function in model training, and the quantitative evaluation metrics of image augmentation. At the same time, we briefly introduce the literature collected in three journals and three conferences that have received attention in medical image processing.
First, we summarize the advantages of various augmentation models, loss functions, and evaluation metrics. Researchers can use this information as a reference when designing augmentation tasks. Second, we explore the relationship between augmented models and the amount of the training set, and tease out the role that augmented models may play when the quality of the training set is limited. Third, the statistical number of papers shows that the development momentum of this research field remains strong. Furthermore, we discuss the existing limitations of this type of model and suggest possible research directions.
We discuss GAN-based medical image augmentation work in detail. This method effectively alleviates the challenge of limited training samples for medical image diagnosis and treatment models. It is hoped that this review will benefit researchers interested in this field.
随着深度学习的发展，基于医学图像的诊断和治疗模型的训练样本数量不断增加。生成对抗网络（GAN）因其出色的图像生成能力而在医学图像处理中受到关注，并已广泛应用于数据增强。本文对医学图像增强工作进行了全面系统的回顾和分析，综述了其研究现状和发展前景。
本文综述了 105 篇与医学图像增强相关的论文，主要通过 ELSEVIER、IEEE Xplore 和 Springer 从 2018 年到 2021 年收集。我们根据与图像对应的器官部分对这些论文进行了计数，并整理了它们中出现的医学图像数据集、模型训练中的损失函数以及图像增强的定量评估指标。同时，我们简要介绍了在医学图像处理中受到关注的三本期刊和三个会议中收集的文献。
首先，我们总结了各种增强模型、损失函数和评估指标的优点。研究人员可以在设计增强任务时参考这些信息。其次，我们探讨了增强模型与训练集数量之间的关系，并探讨了在训练集质量有限时增强模型可能发挥的作用。第三，论文数量的统计表明，该研究领域的发展势头依然强劲。此外，我们讨论了该类模型的现有局限性，并提出了可能的研究方向。
我们详细讨论了基于 GAN 的医学图像增强工作。这种方法有效地缓解了医学图像诊断和治疗模型训练样本有限的挑战。希望本文的综述能为对此领域感兴趣的研究人员提供帮助。
