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Deep Learning-Based Artificial Intelligence for Mammography

Authors
 Jung Hyun Yoon  ;  Eun Kyung Kim 
Citation
 KOREAN JOURNAL OF RADIOLOGY, Vol.22(8) : 1225-1239, 2021-08 
Journal Title
KOREAN JOURNAL OF RADIOLOGY
ISSN
 1229-6929 
Issue Date
2021-08
Keywords
Artificial intelligence ; Breast cancer ; Computer-aided diagnosis ; Deep learning ; Mammography
Abstract
During the past decade, researchers have investigated the use of computer-aided mammography interpretation. With the application of deep learning technology, artificial intelligence (AI)-based algorithms for mammography have shown promising results in the quantitative assessment of parenchymal density, detection and diagnosis of breast cancer, and prediction of breast cancer risk, enabling more precise patient management. AI-based algorithms may also enhance the efficiency of the interpretation workflow by reducing both the workload and interpretation time. However, more in-depth investigation is required to conclusively prove the effectiveness of AI-based algorithms. This review article discusses how AI algorithms can be applied to mammography interpretation as well as the current challenges in its implementation in real-world practice.
Files in This Item:
T202103294.pdf Download
DOI
10.3348/kjr.2020.1210
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Eun-Kyung(김은경) ORCID logo https://orcid.org/0000-0002-3368-5013
Yoon, Jung Hyun(윤정현) ORCID logo https://orcid.org/0000-0002-2100-3513
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/184619
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