A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution
- Submitting institution
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The University of Warwick
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 6087
- Type
- D - Journal article
- DOI
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10.1109/TBME.2014.2303294
- Title of journal
- IEEE Transactions on Biomedical Engineering
- Article number
- -
- First page
- 1729
- Volume
- 61
- Issue
- 6
- ISSN
- 0018-9294
- Open access status
- Out of scope for open access requirements
- Month of publication
- June
- Year of publication
- 2014
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
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A - Applied Computing
- Citation count
- 210
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This influential article was the first one to propose a machine learning based solution to the problem of stain variation. The algorithm devised in this work has been used as a benchmark to compare with most other state-of-the-art algorithms for stain normalization since 2014. The Stain Normalization toolbox released with this publication has spurred subsequent research in the new area of computational pathology, including further developments on machine learning based algorithms for stain normalization by internationally leading groups (e.g. Madabhushi, Case; Tannenbaum, Stony Brook; van der Laak, Radbound MC; Plataniotis, Toronto; Merhof, Aachen).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -