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.

The interim connection space based on human color vision for spectral color reproduction.

To improve the spectral image color reproduction accuracy, two novel interim connection spaces (ICSs) were proposed. The dominant structure of spectral power distributions was extracted by principal component analysis for the widely used illuminants and light sources, and then further transformed to three synthetic illuminants. The CIEXYZ tristimulus under two or three synthetic illuminants was employed to construct two novel ICSs.