Chen Ying, Lin Hongping, Zhang Wei, Feng Longfeng, Zheng Cheng, Zhou Taohui, Yi Zhen, Liu Lan
School of Software, Nanchang Hangkong University, Nanchang 330063, P. R. China.
Department of Medical Imaging, Jiangxi Cancer Hospital, Nanchang 330029, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2023 Feb 25;40(1):185-192. doi: 10.7507/1001-5515.202206039.
Computer-aided diagnosis (CAD) systems play a very important role in modern medical diagnosis and treatment systems, but their performance is limited by training samples. However, the training samples are affected by factors such as imaging cost, labeling cost and involving patient privacy, resulting in insufficient diversity of training images and difficulty in data obtaining. Therefore, how to efficiently and cost-effectively augment existing medical image datasets has become a research hotspot. In this paper, the research progress on medical image dataset expansion methods is reviewed based on relevant literatures at home and abroad. First, the expansion methods based on geometric transformation and generative adversarial networks are compared and analyzed, and then improvement of the augmentation methods based on generative adversarial networks are emphasized. Finally, some urgent problems in the field of medical image dataset expansion are discussed and the future development trend is prospected.
计算机辅助诊断（CAD）系统在现代医疗诊断和治疗系统中发挥着非常重要的作用，但其性能受到训练样本的限制。然而，训练样本受到成像成本、标注成本以及涉及患者隐私等因素的影响，导致训练图像的多样性不足且数据获取困难。因此，如何高效且经济地扩充现有的医学图像数据集已成为一个研究热点。本文基于国内外相关文献，综述了医学图像数据集扩充方法的研究进展。首先，对基于几何变换和生成对抗网络的扩充方法进行了比较和分析，然后着重介绍了基于生成对抗网络的扩充方法的改进。最后，讨论了医学图像数据集扩充领域的一些紧迫问题，并对未来的发展趋势进行了展望。
