乳腺摄影术
深度学习
人工智能
医学
背景(考古学)
机器学习
数字乳腺摄影术
医学物理学
卷积神经网络
乳房成像
乳腺癌
计算机科学
癌症
生物
内科学
古生物学
作者
Krzysztof J. Geras,Ritse M. Mann,Linda Moy
出处
期刊:Radiology
[Radiological Society of North America]
日期:2019-11-01
卷期号:293 (2): 246-259
被引量:210
标识
DOI:10.1148/radiol.2019182627
摘要
Although computer-aided diagnosis (CAD) is widely used in mammography, conventional CAD programs that use prompts to indicate potential cancers on the mammograms have not led to an improvement in diagnostic accuracy. Because of the advances in machine learning, especially with use of deep (multilayered) convolutional neural networks, artificial intelligence has undergone a transformation that has improved the quality of the predictions of the models. Recently, such deep learning algorithms have been applied to mammography and digital breast tomosynthesis (DBT). In this review, the authors explain how deep learning works in the context of mammography and DBT and define the important technical challenges. Subsequently, they discuss the current status and future perspectives of artificial intelligence–based clinical applications for mammography, DBT, and radiomics. Available algorithms are advanced and approach the performance of radiologists—especially for cancer detection and risk prediction at mammography. However, clinical validation is largely lacking, and it is not clear how the power of deep learning should be used to optimize practice. Further development of deep learning models is necessary for DBT, and this requires collection of larger databases. It is expected that deep learning will eventually have an important role in DBT, including the generation of synthetic images. © RSNA, 2019 Online supplemental material is available for this article.
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