Breast cancer early detection is crucial for improving patient outcomes. The Institut Catal\`a de la Salut (ICS) has launched the DigiPatICS project to develop and implement artificial intelligence algorithms to assist with the diagnosis of cancer. In this paper, we propose a new approach for facing the color normalization problem in HER2-stained histopathological images of breast cancer tissue, posed as an style transfer problem. We combine the Color Deconvolution technique with the Pix2Pix GAN network to present a novel approach to correct the color variations between different HER2 stain brands. Our approach focuses on maintaining the HER2 score of the cells in the transformed images, which is crucial for the HER2 analysis. Results demonstrate that our final model outperforms the state-of-the-art image style transfer methods in maintaining the cell classes in the transformed images and is as effective as them in generating realistic images.

翻译：乳腺癌早期检测对于改善患者预后至关重要。加泰罗尼亚健康研究所（ICS）启动了DigiPatICS项目，旨在开发并实施人工智能算法以辅助癌症诊断。本文针对HER2染色乳腺癌组织病理图像中的颜色标准化问题，提出一种基于风格迁移的新方法。我们结合颜色反卷积技术与Pix2Pix生成对抗网络，提出一种创新方案来校正不同HER2染色品牌间的颜色差异。该方法重点关注在转换图像中维持细胞的HER2评分，这对HER2分析至关重要。结果表明，本模型在保持转换后图像细胞类别方面优于当前最先进的图像风格迁移方法，且在生成逼真图像方面具有同等效能。