An open-access breast lesion ultrasound image database: Applicable in artificial intelligence studies
Article
Ardakani, Ali Abbasian, Mohammadi, Afshin, Mirza-Aghazadeh-Attari, Mohammad and Acharya, U. Rajendra. 2023. "An open-access breast lesion ultrasound image database: Applicable in artificial intelligence studies." Computers in Biology and Medicine. 152. https://doi.org/10.1016/j.compbiomed.2022.106438
Article Title | An open-access breast lesion ultrasound image database: Applicable in artificial intelligence studies |
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ERA Journal ID | 5040 |
Article Category | Article |
Authors | Ardakani, Ali Abbasian, Mohammadi, Afshin, Mirza-Aghazadeh-Attari, Mohammad and Acharya, U. Rajendra |
Journal Title | Computers in Biology and Medicine |
Journal Citation | 152 |
Article Number | 106438 |
Number of Pages | 3 |
Year | 2023 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISSN | 0010-4825 |
1879-0534 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.compbiomed.2022.106438 |
Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S0010482522011465 |
Abstract | Breast cancer is one of the largest single contributors to the burden of disease worldwide. Early detection of breast cancer has been shown to be associated with better overall clinical outcomes. Ultrasonography is a vital imaging modality in managing breast lesions. In addition, the development of computer-aided diagnosis (CAD) systems has further enhanced the importance of this imaging modality. Proper development of robust and reproducible CAD systems depends on the inclusion of different data from different populations and centers to considerate all variations in breast cancer pathology and minimize confounding factors. The current database contains ultrasound images and radiologist-defined masks of two sets of histologically proven benign and malignant lesions. Using this and similar pieces of data can aid in the development of robust CAD systems. |
Keywords | Artificial intelligence; Breast cancer ; Deep learning ; Diagnostics; Machine learning ; Radiology; Ultrasound; Segmentation |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Shahid Beheshti University of Medical Sciences, Iran |
Urmia University of Medical Science, Iran | |
Johns Hopkins University, United States | |
Singapore University of Social Sciences (SUSS), Singapore | |
Ngee Ann Polytechnic, Singapore | |
Asia University, Taiwan |
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https://research.usq.edu.au/item/z1v44/an-open-access-breast-lesion-ultrasound-image-database-applicable-in-artificial-intelligence-studies
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