Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks
- Submitting institution
-
Aberystwyth University / Prifysgol Aberystwyth
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 29219623
- Type
- D - Journal article
- DOI
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10.1109/JBHI.2017.2731873
- Title of journal
- IEEE Journal of Biomedical and Health Informatics
- Article number
- -
- First page
- 1218
- Volume
- 22
- Issue
- 4
- ISSN
- 2168-2194
- Open access status
- Compliant
- Month of publication
- August
- 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
- No
- Number of additional authors
-
7
- Research group(s)
-
-
- Citation count
- 118
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- One of the first papers to use deep learning for the segmentation of mammographic abnormalities in ultrasound data, having become a bench-mark in the field. Since publication, the work has been translated to other application areas, such as: diabetic foot ulcer classification (Manchester Metropolitan University), lung cancer (Eindhoven University of Technology), skin cancer (University of Tsukuba, Pázmány Péter Catholic University), liver cancer (Universidad de Córdoba, Kyoto University), and brachytherapy (University of British Columbia). The paper has been in the top 10 most frequently accessed documents for the journal for the past three years.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -