Context-based normalization of histological stains using deep convolutional features
Bug D, Schneider S, Grote A, Oswald E, Feuerhake F, Schueler J, Merhof D (2017)
Publication Type: Conference contribution
Publication year: 2017
Journal
Publisher: Springer Verlag
Book Volume: 10553 LNCS
Pages Range: 135-142
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Quebec City, QC, CAN
ISBN: 9783319675572
DOI: 10.1007/978-3-319-67558-9_16
Abstract
While human observers are able to cope with variations in color and appearance of histological stains, digital pathology algorithms commonly require a well-normalized setting to achieve peak performance, especially when a limited amount of labeled data is available. This work provides a fully automated, end-to-end learning-based setup for normalizing histological stains, which considers the texture context of the tissue. We introduce Feature Aware Normalization, which extends the framework of batch normalization in combination with gating elements from Long Short-Term Memory units for normalization among different spatial regions of interest. By incorporating a pretrained deep neural network as a feature extractor steering a pixelwise processing pipeline, we achieve excellent normalization results and ensure a consistent representation of color and texture. The evaluation comprises a comparison of color histogram deviations, structural similarity and measures the color volume obtained by the different methods.
Involved external institutions
Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen
Germany (DE)
Medizinische Hochschule Hannover (MHH) / Hannover Medical School
Germany (DE)
Oncotest GmbH
Germany (DE)
Germany (DE)
Medizinische Hochschule Hannover (MHH) / Hannover Medical School
Germany (DE)
Oncotest GmbH
Germany (DE)
How to cite
APA:
Bug, D., Schneider, S., Grote, A., Oswald, E., Feuerhake, F., Schueler, J., & Merhof, D. (2017). Context-based normalization of histological stains using deep convolutional features. In Tal Arbel, M. Jorge Cardoso (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 135-142). Quebec City, QC, CAN: Springer Verlag.
MLA:
Bug, D., et al. "Context-based normalization of histological stains using deep convolutional features." Proceedings of the 3rd International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017 and 7th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017, Quebec City, QC, CAN Ed. Tal Arbel, M. Jorge Cardoso, Springer Verlag, 2017. 135-142.
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