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A Review of Artificial Intelligence in Breast Imaging
被引:1
|作者:
Al-Karawi, Dhurgham
[1
]
Al-Zaidi, Shakir
[1
]
Helael, Khaled Ahmad
[2
]
Obeidat, Naser
[3
]
Mouhsen, Abdulmajeed Mounzer
[3
]
Ajam, Tarek
[3
]
Alshalabi, Bashar A.
[3
]
Salman, Mohamed
[3
]
Ahmed, Mohammed H.
[4
]
机构:
[1] Med Analyt Ltd, 26a Castle Pk Ind Pk, Flint CH6 5XA, Wales
[2] King Hussein Med Hosp, Royal Med Serv, King Abdullah 2Ben Al Hussein St, Amman 11855, Jordan
[3] Jordan Univ Sci & Technol, Fac Med, Dept Diagnost Radiol & Nucl Med, Irbid 22110, Jordan
[4] Coventry Univ, Sch Comp, 3 Gulson Rd, Coventry CV1 5FB, England
来源:
关键词:
artificial intelligence network;
deep learning;
machine learning;
breast cancer;
ultrasound image;
mammography image;
COMPUTER-AIDED DIAGNOSIS;
MASS SEGMENTATION;
LESION DETECTION;
NEURAL-NETWORKS;
CANCER;
ULTRASOUND;
CLASSIFICATION;
MAMMOGRAMS;
DATABASE;
SYSTEM;
D O I:
10.3390/tomography10050055
中图分类号:
R8 [特种医学];
R445 [影像诊断学];
学科分类号:
1002 ;
100207 ;
1009 ;
摘要:
With the increasing dominance of artificial intelligence (AI) techniques, the important prospects for their application have extended to various medical fields, including domains such as in vitro diagnosis, intelligent rehabilitation, medical imaging, and prognosis. Breast cancer is a common malignancy that critically affects women's physical and mental health. Early breast cancer screening-through mammography, ultrasound, or magnetic resonance imaging (MRI)-can substantially improve the prognosis for breast cancer patients. AI applications have shown excellent performance in various image recognition tasks, and their use in breast cancer screening has been explored in numerous studies. This paper introduces relevant AI techniques and their applications in the field of medical imaging of the breast (mammography and ultrasound), specifically in terms of identifying, segmenting, and classifying lesions; assessing breast cancer risk; and improving image quality. Focusing on medical imaging for breast cancer, this paper also reviews related challenges and prospects for AI.
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页码:705 / 726
页数:22
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