An End-to-End Deep Learning Histochemical Scoring System for Breast Cancer TMA
- Submitting institution
-
The University of Westminster
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- q7z4x
- Type
- D - Journal article
- DOI
-
10.1109/TMI.2018.2868333
- Title of journal
- IEEE Transactions on Medical Imaging
- Article number
- -
- First page
- 617
- Volume
- 38
- Issue
- 2
- ISSN
- 0278-0062
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2018
- 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
-
8
- Research group(s)
-
-
- Citation count
- 7
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Immunohistochemical scores in breast cancer tumour tissues are manually derived by pathologists in a lengthy and expensive manner. An automated approach was long overdue to allow faster and more rigorous scoring. This work is significant, because it presents for the first time an automated system that can be used to derive histochemical scores, which can then be used to categorise breast cancer patients into biological classes. This proof of principle study has the potential to generate great impact, as it demonstrates that time and money can be saved in the NHS by using deep learning automated systems.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
