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Jpn J Radiol. 2018 Apr;36(4):257-272. doi: 10.1007/s11604-018-0726-3. Epub 2018 Mar 1.

Deep learning with convolutional neural network in radiology.

Author information

1
Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan. koyasaka@gmail.com.
2
Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan.
3
Department of Radiology, Graduate School of Medical Sciences, International University of Health and Welfare, 4-3 Kozunomori, Narita, Chiba, Japan.
4
Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan.

Abstract

Deep learning with a convolutional neural network (CNN) is gaining attention recently for its high performance in image recognition. Images themselves can be utilized in a learning process with this technique, and feature extraction in advance of the learning process is not required. Important features can be automatically learned. Thanks to the development of hardware and software in addition to techniques regarding deep learning, application of this technique to radiological images for predicting clinically useful information, such as the detection and the evaluation of lesions, etc., are beginning to be investigated. This article illustrates basic technical knowledge regarding deep learning with CNNs along the actual course (collecting data, implementing CNNs, and training and testing phases). Pitfalls regarding this technique and how to manage them are also illustrated. We also described some advanced topics of deep learning, results of recent clinical studies, and the future directions of clinical application of deep learning techniques.

KEYWORDS:

CT; Convolutional neural network; Deep learning; MRI; PET

PMID:
29498017
DOI:
10.1007/s11604-018-0726-3
[Indexed for MEDLINE]

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