Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images
- Submitting institution
-
The University of Warwick
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 10575
- Type
- D - Journal article
- DOI
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10.1109/TMI.2016.2525803
- Title of journal
- IEEE Transactions on Medical Imaging
- Article number
- -
- First page
- 1196
- Volume
- 35
- Issue
- 5
- ISSN
- 0278-0062
- Open access status
- Out of scope for open access requirements
- Month of publication
- May
- Year of publication
- 2016
- 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
-
5
- Research group(s)
-
A - Applied Computing
- Citation count
- 388
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Published in the top medical imaging journal, this paper presents the first deep-learning approach to detect and classify cells in whole-slide images of cancer tissues using spatially constrained regression. A preliminary version won the Best Paper Award at MICCAI-PatchMI 2015. Follow-up work was pivotal in securing MRC funding for the MiCAHiL project (MR/P015476/1). Further research by a team of scientists at ICR (Yuan lab), Crick (Swanton lab) and UCL (Quezada) has used this work in the lung TracerX study to analyse lung cancer evolution and predict its progression by studying interaction of various cells in the tumour microenvironment.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -