Learning spatiotemporal features for esophageal abnormality detection from endoscopic videos
- Submitting institution
-
The University of West London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11025
- Type
- D - Journal article
- DOI
-
10.1109/JBHI.2020.2995193
- Title of journal
- IEEE Journal of Biomedical and Health Informatics
- Article number
- -
- First page
- 131
- Volume
- 25
- Issue
- 1
- ISSN
- 2168-2194
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2020
- 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)
-
-
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper is based on 4 years of collaborative research on detection of Esophageal cancer in collaboration with colleagues at the University of Lincoln (contact: Prof Xujiong Ye). The work is significant because rather than using isolated frames, this is the first study of its kind to use both temporal and spatial information available in endoscopic videos, to propose an innovative and effective approach for the detection of different types of oesophageal abnormalities. The proposed method is comprehensively evaluated in this study.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -