Research progress on medical image dataset expansion methods

计算机辅助诊断（CAD）系统对现代医学诊疗体系具有非常重要的作用，但其性能受训练样本的限制。而训练样本受成像成本、标记成本和涉及患者隐私等因素的影响，导致训练图像多样性不足且难以获取。因此，如何高效且以较低成本扩充现有医学图像数据集成为研究的热点。本文结合国内外的相关文献，对医学图像数据集扩充方法的研究进展进行综述，首先对比分析基于几何变换和基于生成对抗网络的扩充方法，其次重点介绍基于生成对抗网络扩充方法的改进及其适用场景，最后讨论医学图像数据集扩充领域的一些亟待解决的问题并对其未来发展趋势进行展望。.; 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.