Japanese Journal of Radiology

, Volume 36, Issue 4, pp 257–272 | Cite as

Deep learning with convolutional neural network in radiology

  • Koichiro Yasaka
  • Hiroyuki Akai
  • Akira Kunimatsu
  • Shigeru Kiryu
  • Osamu Abe
Review
  • 444 Downloads

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

Deep learning Convolutional neural network CT MRI PET 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical statement

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Japan Radiological Society 2018

Authors and Affiliations

  1. 1.Department of Radiology, The Institute of Medical ScienceThe University of TokyoTokyoJapan
  2. 2.Department of Radiology, Graduate School of Medical SciencesInternational University of Health and WelfareNaritaJapan
  3. 3.Department of Radiology, Graduate School of MedicineThe University of TokyoTokyoJapan

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