Preservation of Image Content in Stain-to-stain Translation for Digital Pathology

In digital pathology, unsupervised domain adaptation of differently stained whole-slide images (WSIs) through image-to-image translation has become increasingly important for various applications such as stain augmentation or for the stain-independent application of deep learning models. In previous work, different variants of generative adversarial networks (GANs) were proposed to translate a real WSI obtained in the staining domain A into a fake WSI in the target staining domain B. However, GANs perform unpaired image-toimage translation and do not enforce consistency with respect to image content, which limits their applicability in digital pathology settings. In this paper, we first investigate the tissue inconsistency problem in such a stain-to-stain translation scenario using a quantitative evaluation of the distortion between real and fake images in different domains. Then, we investigate two possible solutions, namely (1) stain colorization inspired by natural image colorization, and (2) a modified Cycle-GAN, where an intensity invariant loss is proposed to balance the tissue consistency across staining domains. Our results highlight the superiority of these methods compared to conventional unpaired stain translation solutions for typical staining protocols in digital pathology.