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Histopathological cancer diagnosis is based on visual examination of stainedtissue slides. Hematoxylin and eosin (H\&E) is a standard stain routinelyemployed worldwide. It is easy to acquire and cost effective, but cells andtissue components show low-contrast with varying tones of dark blue and pink,which makes difficult visual assessments, digital image analysis, andquantifications. These limitations can be overcome by IHC staining of targetproteins of the tissue slide. IHC provides a selective, high-contrast imagingof cells and tissue components, but their use is largely limited by asignificantly more complex laboratory processing and high cost. We proposed aconditional CycleGAN (cCGAN) network to transform the H\&E stained images intoIHC stained images, facilitating virtual IHC staining on the same slide. Thisdata-driven method requires only a limited amount of labelled data but willgenerate pixel level segmentation results. The proposed cCGAN model improvesthe original network \cite{zhu_unpaired_2017} by adding category conditions andintroducing two structural loss functions, which realize a multi-subdomaintranslation and improve the translation accuracy as well. % need to givereasons here. Experiments demonstrate that the proposed model outperforms theoriginal method in unpaired image translation with multi-subdomains. We alsoexplore the potential of unpaired images to image translation method applied onother histology images related tasks with different staining techniques.

GAN-based Virtual Re-Staining: A Promising Solution for Whole Slide Image Analysis