Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results from the MICCAI 2015 Endoscopic Vision Challenge
- Submitting institution
-
University of Central Lancashire
- Unit of assessment
- 12 - Engineering
- Output identifier
- 17023
- Type
- D - Journal article
- DOI
-
10.1109/TMI.2017.2664042
- Title of journal
- IEEE Transactions on Medical Imaging
- Article number
- -
- First page
- 1231
- Volume
- 36
- Issue
- 6
- ISSN
- 0278-0062
- Open access status
- Compliant
- Month of publication
- June
- Year of publication
- 2017
- 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
- Yes
- Number of additional authors
-
23
- Research group(s)
-
H - Computer Vision and Machine Learning Group
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The paper reports the efforts of an international collaboration to develop and evaluate state-of-the-art techniques in video colonoscopy analysis. Knowledge derived contributed to UCLan subsequent successes at international challenges, including two 1st places at the Endoscopic Vision Challenges. The study was instrumental in extending research with ETIS-CNRS and developing collaboration with Sorbonne University leading to a research grant from the STFC CDN+ on Machine Learning Automation in Colonoscopy and submission of a CRUK grant application with ETIS, UAB, Oxford, and Sorbonne. The reported research contributed to completion of 1 PhD, 5 papers and a forthcoming book on endoscopy video analysis.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
