  Although generative adversarial network (GAN) based style transfer is state
of the art in histopathology color-stain normalization, they do not explicitly
integrate structural information of tissues. We propose a self-supervised
approach to incorporate semantic guidance into a GAN based stain normalization
framework and preserve detailed structural information. Our method does not
require manual segmentation maps which is a significant advantage over existing
methods. We integrate semantic information at different layers between a
pre-trained semantic network and the stain color normalization network. The
proposed scheme outperforms other color normalization methods leading to better
classification and segmentation performance.
