Structure Preserving Stain Normalization of Histopathology Images Using Self Supervised Semantic Guidance
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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.
Color normalization, Digital pathology, GANs, Semantic guidance
D. Mahapatra, B. Bozorgtabar, JP Thiran, and L. Shao, "Structure Preserving Stain Normalization of Histopathology Images Using Self Supervised Semantic Guidance", In Medical Image Computing and Computer Assisted Intervention (MICCAI 2020), Lecture Notes in Computer Science, vol 12265, Sept 2020, doi:10.1007/978-3-030-59722-1_30