Structure Preserving Stain Normalization of Histopathology Images Using Self-Supervised Semantic Guidance
This addresses the need for accurate stain normalization in histopathology images without requiring manual segmentation, which is incremental as it builds on existing GAN-based approaches.
The paper tackles the problem of preserving structural information in histopathology stain normalization by proposing a self-supervised semantic guidance method integrated into a GAN framework, resulting in improved classification and segmentation performance compared to other methods.
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.