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Convolutional Neural Network Technology in Endoscopic Imaging: Artificial Intelligence for Endoscopy

Clinical Endoscopy 2020년 53권 2호 p.117 ~ 126
Choi Joon-Myeong, Shin Kee-Won, 정진훈, Bae Hyun-Jin, 김도훈, 변정식, 김남국,
소속 상세정보
 ( Choi Joon-Myeong ) - University of Ulsan College of Medicine Department of Convergence Medicine
 ( Shin Kee-Won ) - University of Ulsan College of Medicine Department of Convergence Medicine
정진훈 ( Jung Jin-Hoon ) - Promedius Inc.
 ( Bae Hyun-Jin ) - Promedius Inc.
김도훈 ( Kim Do-Hoon ) - University of Ulsan College of Medicine Asan Medical Center Department of Gastroenterology
변정식 ( Byeon Jeong-Sik ) - University of Ulsan College of Medicine Asan Medical Center Department of Gastroenterology
김남국 ( Kim Nam-Kug ) - University of Ulsan College of Medicine Asan Medical Center Department of Radiology

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.

키워드

Artificial intelligence; Convolutional neural network; Deep learning; Endoscopic imaging; Machine learning

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