Convolutional Neural Network Technology in Endoscopic Imaging: Artificial Intelligence for Endoscopy
Clinical Endoscopy 2020³â 53±Ç 2È£ p.117 ~ p.126
(Choi Joon-Myeong) - University of Ulsan College of Medicine Department of Convergence Medicine
½Å±â¿ø(Shin Kee-Won) - University of Ulsan College of Medicine Department of Convergence Medicine
Á¤ÁøÈÆ(Jung Jin-Hoon) - Promedius Inc.
¹èÇöÁø(Bae Hyun-Jin) - Promedius Inc.
±èµµÈÆ(Kim Do-Hoon) - University of Ulsan College of Medicine Asan Medical Center Department of Gastroenterology
º¯Á¤½Ä(Byeon Jeong-Sik) - University of Ulsan College of Medicine Asan Medical Center Department of Gastroenterology
±è³²±¹(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.
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Artificial intelligence, Convolutional neural network, Deep learning, Endoscopic imaging, Machine learning
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À¯È¿¼º°á°ú(Recomendation)
Recent studies in this technology augur well for medical and healthcare applications, especially in endoscopic imaging.