  The diagnosis of cancer is mainly performed by visual analysis of the
pathologists, through examining the morphology of the tissue slices and the
spatial arrangement of the cells. If the microscopic image of a specimen is not
stained, it will look colorless and textured. Therefore, chemical staining is
required to create contrast and help identify specific tissue components.
During tissue preparation due to differences in chemicals, scanners, cutting
thicknesses, and laboratory protocols, similar tissues are usually varied
significantly in appearance. This diversity in staining, in addition to
Interpretive disparity among pathologists more is one of the main challenges in
designing robust and flexible systems for automated analysis. To address the
staining color variations, several methods for normalizing stain have been
proposed. In our proposed method, a Stain-to-Stain Translation (STST) approach
is used to stain normalization for Hematoxylin and Eosin (H&E) stained
histopathology images, which learns not only the specific color distribution
but also the preserves corresponding histopathological pattern. We perform the
process of translation based on the pix2pix framework, which uses the
conditional generator adversarial networks (cGANs). Our approach showed
excellent results, both mathematically and experimentally against the state of
the art methods. We have made the source code publicly available.
