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

Color Deconvolution applied to Domain Adaptation in HER2 histopathological images