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

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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