Color Deconvolution applied to Domain Adaptation in HER2
  histopathological images
David Anglada-Rotger Ferran Marqués Montse Pardàs
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.