https://www.e-ce.org/journal/view.php?number=7342
| Clin Endosc > Volume 53(2); 2020 > Article |
|
Focused Review Series: Application of Artificial Intelligence in GI Endoscopy
Clin Endosc 2020; 53(2): 117-126.
Published online: March 30, 2020
DOI: https://doi.org/10.5946/ce.2020.054
Convolutional Neural Network Technology in Endoscopic Imaging: Artificial Intelligence for Endoscopy
, Keewon Shin1
, Jinhoon Jung2
, Hyun-Jin Bae2
, Do Hoon Kim3
, Namku Kim1,4
1Department of Convergence Medicine, University of Ulsan College of Medicine, Seoul, Korea
2Promedius, Inc., Seoul, Korea
3Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
4Department of Radiology, Asan Medical Center, Seoul, Korea
Correspondence: Namkug Kim Department of Radiology, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea
Tel: +82-2-3010-6573, Fax: +82-2-2045-3426, E-mail: namkugkim@gmail.com
Received February 21, 2020 Revised March 10, 2020 Accepted March 13, 2020
Copyright © 2020 Korean Society of Gastrointestinal Endoscopy
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Recently, significant improvements have been made in artificial intelligence. The artificial neural network was introduced in the 1950s. However, because of the low computing power and insufficient datasets available at that time, artificial neural networks suffered from overfitting and vanishing gradient problems for training deep networks. This concept has become more promising owing to the enhanced big data processing capability, improvement in computing power with parallel processing units, and new algorithms for deep neural networks, which are becoming increasingly successful and attracting interest in many domains, including computer vision, speech recognition, and natural language processing. Recent studies in this technology augur well for medical and healthcare applications, especially in endoscopic imaging. This paper provides perspectives on the history, development, applications, and challenges of deep-learning technology.
Key words: Artificial intelligence; Convolutional neural network; Deep learning; Endoscopic imaging; Machine learning
아산병원, Promedius.
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