Staingan: Stain style transfer for digital histological images
Shaban MT, Baur C, Navab N, Albarqouni S (2019)
Publication Type: Conference contribution
Publication year: 2019
Journal
Publisher: IEEE Computer Society
Book Volume: 2019-April
Pages Range: 953-956
Conference Proceedings Title: Proceedings - International Symposium on Biomedical Imaging
Event location: Venice, ITA
ISBN: 9781538636411
DOI: 10.1109/ISBI.2019.8759152
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.
Involved external institutions
Germany (DE)How to cite
APA:
Shaban, M.T., Baur, C., Navab, N., & Albarqouni, S. (2019). Staingan: Stain style transfer for digital histological images. In Proceedings - International Symposium on Biomedical Imaging (pp. 953-956). Venice, ITA: IEEE Computer Society.
MLA:
Shaban, M. Tarek, et al. "Staingan: Stain style transfer for digital histological images." Proceedings of the 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019, Venice, ITA IEEE Computer Society, 2019. 953-956.
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