Application of CycleGAN and ACGAN in Artificial Intelligence Medical Device
Data Augmentation
HAO Pengfei1, LI Yao1, CHAI Rui1, PEI Xiaojuan1, YU Zhe1, LI Qingyu1, CHEN Xi2, ZHANG Ke1
1. Center of Medical Electrical Quality Evaluation, Shandong Institute of Medical Device and Pharmaceutical Packaging Inspection,
Jinan Shandong 250101, China; 2. Top Information Technology Co., Ltd., Jinan Shandong 250101, China
Abstract：Objective To explore the method of data augmentation using cycle-consistent generative adversarial networks
(CycleGAN) and auxiliary classification generative adversarial network (ACGAN) in artificial intelligence medical devices.
Methods The CycleGAN and ACGAN were used to generate interference images and specific domain data, respectively.
Irregular transformations were applied to the images to augment them, and the original image data was processed or fed into
generative adversarial networks to generate the required image data for that particular domain. Results The performance
was evaluated on a medical imaging dataset, and the results showed that CycleGAN and ACGAN could effectively generate
realistic medical images that could be used to train machine learning models. Conclusion This method can solve the problem
of insufficient image data in the field of artificial intelligence, while ensuring the invisibility of the data to the model, making
the later model evaluation results more accurate.
郝鹏飞1，李瑶1，柴蕊1，裴晓娟1，于哲1，李庆雨1，陈曦2，张克1. CycleGAN、ACGAN在人工智能
医疗器械数据增广中的应用[J]. 中国医疗设备, 2024, 39(2): 52-56.
HAO Pengfei1, LI Yao1, CHAI Rui1, PEI Xiaojuan1, YU Zhe1, LI Qingyu1, CHEN Xi2, ZHANG Ke1. Application of CycleGAN and ACGAN in Artificial Intelligence Medical Device
Data Augmentation. China Medical Devices, 2024, 39(2): 52-56.
