Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks
- Submitting institution
-
Manchester Metropolitan University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2338
- Type
- D - Journal article
- DOI
-
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
-
https://e-space.mmu.ac.uk/619005/
- 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)
-
B - Human Centred-Computing
- Citation count
- 118
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper is a game-changer in breast ultrasound research forming a baselines that is fully reproducible. It was the first time a deep neural network was introduced to breast ultrasound lesions detection alongside a clinical annotated dataset with over 230 users from 36 countries. This paper led to a continuous international collaboration with University of Girona, Aberystwyth University, Norfolk and Norwich Teaching Hospital, producing 2 co-supervised PhDs, a Cancer Research UK grant (C52190/A30051) and new collaborations in Bangladesh, Malaysia and Vietnam. It is one of the top 3 most popular articles in the journal (2019).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -