An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy
- Submitting institution
-
University of Oxford
- Unit of assessment
- 12 - Engineering
- Output identifier
- 9528
- Type
- D - Journal article
- DOI
-
10.1038/s41598-020-59413-5
- Title of journal
- Scientific reports
- Article number
- ARTN 2748
- First page
- 2748
- Volume
- 10
- Issue
- 1
- ISSN
- 2045-2322
- Open access status
- Compliant
- Month of publication
- February
- Year of publication
- 2020
- URL
-
-
- Supplementary information
-
https://static-content.springer.com/esm/art%3A10.1038%2Fs41598-020-59413-5/MediaObjects/41598_2020_59413_MOESM1_ESM.pdf
- 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
-
28
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The Endoscopy Artefact Detection (EAD) challenge (https://ead2020.grand-challenge.org/) is one of the largest and comprehensive publicly available data sets for benchmarking algorithms that automatically assess quality of endoscopy video. The paper provides an in-depth analysis of the algorithms submitted from 29 teams to the 2019 EAD workshop. The paper establishes a set of benchmarks for a procedure performed over 2 million times annually in the UK. In addition, the paper outlines challenges the field needs to address. With an altmetric score of 14, it is ranked in the 95th percentile of all articles of a similar age in Scientific Reports.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -