Application of CycleGAN and ACGAN in Artificial Intelligence Medical Device Data Augmentation
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.
万方数据
维普
