Generating synthetic medical images with limited data using auxiliary classifier generative adversarial network: a study on thyroid ultrasound images.

Background And Objective: The availability of labeled data is crucial for training deep neural networks. However, in some cases, the available data is limited or unlabeled, which poses a significant obstacle in developing accurate models. Various approaches exist to address this issue, such as Image Augmentation, Transfer Learning, and GANs.