Pseudo-labeling generative adversarial networks for medical image classification.

Semi-supervised learning has become a popular technology in recent years. In this paper, we propose a novel semi-supervised medical image classification algorithm, called Pseudo-Labeling Generative Adversarial Networks (PLGAN), which only uses a small number of real images with few labels to generate fake images or mask images to enlarge the sample size of the labeled training set. First, we combine MixMatch to generate pseudo labels for the fake and unlabeled images to do the classification.