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Korean J Radiol. 2017 Jul-Aug;18(4):570-584. doi: 10.3348/kjr.2017.18.4.570. Epub 2017 May 19.

Deep Learning in Medical Imaging: General Overview.

Lee JG1, Jun S2,3, Cho YW2,3, Lee H2,3, Kim GB2,3, Seo JB2, Kim N2,3.

Author information

1
Biomedical Engineering Research Center, University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505, Korea.
2
Department of Radiology, Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505, Korea.
3
Department of Convergence Medicine, Biomedical Engineering Research Center, University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505, Korea.

Abstract

The artificial neural network (ANN)-a machine learning technique inspired by the human neuronal synapse system-was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and healthcare, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging.

KEYWORDS:

Artificial intelligence; Computer-aided; Convolutional neural network; Machine learning; Precision medicine; Radiology; Recurrent Neural Network

PMID:
28670152
PMCID:
PMC5447633
DOI:
10.3348/kjr.2017.18.4.570
[Indexed for MEDLINE]
Free PMC Article

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