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Document type:
Zeitschriftenaufsatz 
Author(s):
Shaban, M. T.; Baur, C.; Navab, N.; Albarqouni, S. 
Title:
StainGAN: Stain Style Transfer for Digital Histological Images 
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 approac...    »
 
Keywords:
isbi,histology,normalization,camp,deeplearning 
Journal title:
arXiv e-prints 
Year:
2019 
Pages contribution:
arXiv--1804