Structure Preserving Stain Normalization of Histopathology Images Using Self Supervised Semantic Guidance
Source of Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
© 2020, Springer Nature Switzerland AG. 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.
Springer International Publishing
Color normalization, Digital pathology, GANs, Semantic guidance
Mahapatra, Dwarikanath; Bozorgtabar, Behzad; Thiran, Jean Philippe; and Shao, Ling, "Structure Preserving Stain Normalization of Histopathology Images Using Self Supervised Semantic Guidance" (2020). All Works. 3235.
Indexed in Scopus
Open Access Type
Green: A manuscript of this publication is openly available in a repository