https://www.e-ce.org/journal/view.php?number=7343
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Focused Review Series: Application of Artificial Intelligence in GI Endoscopy
Clin Endosc 2020; 53(2): 127-131. Published online: March 30, 2020 DOI: https://doi.org/10.5946/ce.2020.046 Lesion-Based Convolutional Neural Network in Diagnosis of Early Gastric Cancer1Division of Gastroenterology, Department of Internal Medicine, Soonchunhyang University College of Medicine, Cheonan, Korea 2Division of Gastroenterology, Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea Correspondence: Jie-Hyun Kim Division of Gastroenterology, Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, 211 Eonju-ro, Gangnamgu, Seoul 06273, Korea Received February 14, 2020 Revised March 13, 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. AbstractDiagnosis and evaluation of early gastric cancer (EGC) using endoscopic images is significantly important; however, it has some limitations. In several studies, the application of convolutional neural network (CNN) greatly enhanced the effectiveness of endoscopy. To maximize clinical usefulness, it is important to determine the optimal method of applying CNN for each organ and disease. Lesion-based CNN is a type of deep learning model designed to learn the entire lesion from endoscopic images. This review describes the application of lesion-based CNN technology in diagnosis of EGC. Key words: Artificial intelligence; Convolutional neural networks; Early gastric cancer; Endoscopy; Invasion depth |
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병변 의심 되는 곳에 Grad_CAM 을 적용했다는 정도..
