论文标题
结构使用自我监督的语义指导保留组织病理学图像的染色标准化
Structure Preserving Stain Normalization of Histopathology Images Using Self-Supervised Semantic Guidance
论文作者
论文摘要
尽管基于生成的对抗性网络（GAN）样式转移是组织病理学颜色剂归一化的艺术状态，但它们并未明确整合组织的结构信息。我们提出了一种自我监督的方法，将语义指导纳入基于GAN的染色归一化框架并保留详细的结构信息。我们的方法不需要手动分割图，这比现有方法是一个重要的优势。我们在预训练的语义网络和染色颜色归一化网络之间在不同层上集成了语义信息。所提出的方案优于其他颜色归一化方法，从而可以更好地分类和分割性能。
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.
