Yizhou Chen1, Xu-Hua Yang2, Zihan Wei3, Ali Asghar Heidari4, Nenggan Zheng5, Zhicheng Li6, Huiling Chen7, Haigen Hu8, Qianwei Zhou9, Qiu Guan10. 1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China. Electronic address: yizhou@zjut.edu.cn. 2. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China. Electronic address: xhyang@zjut.edu.cn. 3. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China. Electronic address: 2112012201@zjut.edu.cn. 4. 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. Electronic address: as_heidari@ut.ac.ir. 5. Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, Zhejiang, China. Electronic address: zng@cs.zju.edu.cn. 6. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. Electronic address: zc.li@siat.ac.cn. 7. College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang, 325035, China. Electronic address: chenhuiling.jlu@gmail.com. 8. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China. Electronic address: hghu@zjut.edu.cn. 9. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China. Electronic address: zqw@zjut.edu.cn. 10. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China. Electronic address: gq@zjut.edu.cn.
Abstract
OBJECT: 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. METHOD: 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. RESULT: 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. CONCLUSION: 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.
OBJECT: 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. METHOD: 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. RESULT: 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. CONCLUSION: 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.