1. Convolutional Neural Network Technology in Endoscopic Imaging: Artificial Intelligence for Endoscopy
- Author
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Choi, Joonmyeong, Shin, Keewon, Jung, Jinhoon, Bae, Hyun-Jin, Kim, Do Hoon, Byeon, Jeong-Sik, and Kim, Namku
- Subjects
Big data processing ,lcsh:Internal medicine ,Artificial intelligence ,Medicine (miscellaneous) ,Convolutional neural network ,Overfitting ,03 medical and health sciences ,Endoscopic imaging ,0302 clinical medicine ,Machine learning ,Medicine ,Radiology, Nuclear Medicine and imaging ,lcsh:RC799-869 ,lcsh:RC31-1245 ,Focused Review Series: Application of Artificial Intelligence in GI Endoscopy ,Artificial neural network ,business.industry ,Deep learning ,Gastroenterology ,Parallel processing (DSP implementation) ,030220 oncology & carcinogenesis ,Deep neural networks ,lcsh:Diseases of the digestive system. Gastroenterology ,030211 gastroenterology & hepatology ,business - 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. Clin Endosc 2020;53:117-126
- Published
- 2020