Staingan: Stain Style Transfer for Digital Histological Images

M. Tarek Shaban, Christoph Baur, Nassir Navab, Shadi Albarqouni

Abstract: 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 a 12% increase in AUC. The code is made publicly available 1.1https://github.com/xtarx/StainGAN

External IDs:dblp:conf/isbi/ShabanBNA19