A quantitative assessment of image normalization for classifying histopathological tissue of the kidney
Gadermayr M, Cooper SS, Klinkhammer B, Boor P, Merhof D (2017)
Publication Type: Conference contribution
Publication year: 2017
Journal
Publisher: Springer Verlag
Book Volume: 10496 LNCS
Pages Range: 3-13
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Basel, CHE
ISBN: 9783319667089
DOI: 10.1007/978-3-319-66709-6_1
Abstract
The advancing pervasion of digital pathology in research and clinical practice results in a strong need for image analysis techniques in the field of histopathology. Due to diverse reasons, histopathological imaging generally exhibits a high degree of variability. As automated segmentation approaches are known to be vulnerable, especially to unseen variability, we investigate several stain normalization methods to compensate for variations between different whole slide images. In a large experimental study, we investigate all combinations of five image normalization (not only stain normalization) methods as well as five image representations with respect to the classification performance in two application scenarios in kidney histopathology. Finally, we also pose the question, if color normalization is sufficient to compensate for the changed properties between whole slide images in an application scenario with few training data.
Involved external institutions
Germany (DE)How to cite
APA:
Gadermayr, M., Cooper, S.S., Klinkhammer, B., Boor, P., & Merhof, D. (2017). A quantitative assessment of image normalization for classifying histopathological tissue of the kidney. In Volker Roth, Thomas Vetter (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 3-13). Basel, CHE: Springer Verlag.
MLA:
Gadermayr, Michael, et al. "A quantitative assessment of image normalization for classifying histopathological tissue of the kidney." Proceedings of the 39th German Conference on Pattern Recognition, GCPR 2017, Basel, CHE Ed. Volker Roth, Thomas Vetter, Springer Verlag, 2017. 3-13.
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