Conference CORE B

Staingan: Stain style transfer for digital histological images

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

2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 2019

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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 cycle consistency 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. We further validated our method on a clinical use-case, namely Breast Cancer tumor classification, showing 16% increase in AUC

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Research themes

Medical ImagingResponsible AI

Keywords

Stain NormalizationGenerative Adversarial NetworksHistologyDomain AdaptationMedical ImagingStyle Transfer

Lab authors

M. Tarek ShabanChristoph BaurNassir NavabShadi Albarqouni
Cite (BibTeX)
@inproceedings{shaban2019staingan,
 author = {Shaban, M Tarek and Baur, Christoph and Navab, Nassir and Albarqouni, Shadi},
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 booktitle = {2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)},
 organization = {IEEE},
 pages = {953--956},
 title = {Staingan: Stain style transfer for digital histological images},
 year = {2019}
}

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