Fast, Self Supervised, Fully Convolutional Color Normalization Of H&E Stained Images

@article{Patil2021FastSS,
  title={Fast, Self Supervised, Fully Convolutional Color Normalization Of H\&E Stained Images},
  author={Abhijeet Patil and Mohd Talha and Aniket Bhatia and Nikhil Cherian Kurian and Sammed Mangale and Sunil Patel and Amit Sethi},
  journal={2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)},
  year={2021},
  pages={1563-1567}
}
  • Abhijeet PatilM. Talha A. Sethi
  • Published 30 November 2020
  • Computer Science
  • 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
Performance of deep learning algorithms decreases drastically if the data distributions of the training and testing sets are different. Due to variations in staining protocols, reagent brands, and habits of technicians, color variation in digital histopathology images is quite common. Color variation causes problems for the deployment of deep learning-based solutions for automatic diagnosis system in histopathology. Previously proposed color normalization methods consider a small patch as a… 

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