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Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

M. T. Shaban, C. Baur, N. Navab, S. Albarqouni
StainGAN: Stain Style Transfer for Digital Histological Images
Submitted to the 21st International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI), Granada, Spain, September 2018
A pre-print version is available online at arXiv.
(bib)

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.
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