AUTHORS: Mahapatra D, Bozorgtabar B, Thiran JP, Shao L

International Conference on Medical Image Computing and Computer-Assisted Intervention-MICCAI, : 309-319, Lima, Peru, October 2020


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.

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