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A method for normalizing histology slides for quantitative analysis
Abstract
Inconsistencies in the preparation of histology slides make it difficult to perform quantitative analysis on their results. In this paper we provide two mechanisms for overcoming many of the known inconsistencies in the staining process, thereby bringing slides that were processed or stored under very different conditions into a common, normalized space to enable improved quantitative analysis.
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“These typically fall into one of two categories: data augmentation methods that perturb the colors in the training images (e.g., Liu et al. (2017); Bug et al. (2017); Tellez et al. (2018)) or stain normalization methods that seek to standardize colors across training images (e.g., Macenko et al. (2009); BenTaieb and Hamarneh (2017)).”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Macenko et al. (2009)
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Metadata
- Authors
- Macenko, M., Niethammer, M., Marron, J. S., Borland, D., Woosley, J. T., Guan, X., Schmitt, C., & Thomas, N. E.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- ORCID
- Venue
- 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro
- Publisher
- IEEE
- Published
- 2009-06
- Pages
- 1107-1110
- OpenAlex
- W2132162500
- Cited by
- 1,482
- References
- 10
- Citations by year
- 2026: 170 · 2025: 198 · 2024: 208 · 2023: 182 · 2022: 190 · 2021: 149 · 2020: 114 · 2019: 91 · 2018: 57 · 2017: 41 · 2016: 28 · 2015: 21 · 2014: 14 · 2013: 6 · 2012: 5
- FWCI
- 2.81
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Symposium on Biomedical Imaging (ISBI) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Imaging for Blood Diseases
- Keywords
- quantitative histology, image normalization, histological image analysis
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