Structure Preserving Stain Normalization of Histopathology Images Using Self Supervised Semantic Guidance

Document Type

Conference Proceeding

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract

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.

First Page

309

Last Page

319

DOI

10.1007/978-3-030-59722-1_30

Publication Date

9-29-2020

Keywords

Color normalization, Digital pathology, GANs, Semantic guidance

Comments

IR conditions: non-described

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