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.

该研究开发了一种新方法，使用人工智能算法解决乳腺癌组织病理学图像HER2染色的颜色标准化问题，该方法将颜色分离方法与Pix2Pix GAN网络相结合，以保持细胞的HER2评分，并在维持细胞类别的同时生成逼真的图像。

颜色去卷积在HER2组织病理图像域适应中的应用