  Digitized Histological diagnosis is in increasing demand. However, color
variations due to various factors are imposing obstacles to the diagnosis
process. The problem of stain color variations is a well-defined problem with
many proposed solutions. Most of these solutions are highly dependent on a
reference template slide. We propose a deep-learning solution inspired by
CycleGANs that is trained end-to-end, eliminating the need for an expert to
pick a representative reference slide. Our approach showed superior results
quantitatively and qualitatively against the state of the art methods (10%
improvement visually using SSIM). We further validated our method on a clinical
use-case, namely Breast Cancer tumor classification, showing 12% increase in
AUC. The code will be made publicly available.
