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J Magn Reson Imaging. 2019 Apr;49(4):939-954. doi: 10.1002/jmri.26534. Epub 2018 Dec 21.

Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI.

Author information

1
Department of Radiology, Duke University, Durham, North Carolina, USA.
2
Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina, USA.
3
Duke Medical Physics Program, Duke University, Durham, North Carolina, USA.
4
Center for Advanced Magnetic Resonance Development, Duke University, Durham, North Carolina, USA.

Abstract

Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems. Deep-learning algorithms have shown groundbreaking performance in a variety of sophisticated tasks, especially those related to images. They have often matched or exceeded human performance. Since the medical field of radiology mainly relies on extracting useful information from images, it is a very natural application area for deep learning, and research in this area has rapidly grown in recent years. In this article, we discuss the general context of radiology and opportunities for application of deep-learning algorithms. We also introduce basic concepts of deep learning, including convolutional neural networks. Then, we present a survey of the research in deep learning applied to radiology. We organize the studies by the types of specific tasks that they attempt to solve and review a broad range of deep-learning algorithms being utilized. Finally, we briefly discuss opportunities and challenges for incorporating deep learning in the radiology practice of the future. Level of Evidence: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:939-954.

KEYWORDS:

artificial intelligence; convolutional neural networks; deep learning; machine learning; medical imaging; radiology

PMID:
30575178
PMCID:
PMC6483404
[Available on 2020-04-01]
DOI:
10.1002/jmri.26534

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