Zobrazeno 1 - 10 of 21 pro vyhledávání: '"Seon-Ho Shin"'
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Akademický článek
Current Status of Mucosal Imaging with Narrow-Band Imaging in the Esophagus
Autor: Keng Hoong Chiam, Seon Ho Shin, Kun Cheong Choi, Florencia Leiria, Mariana Militz, Rajvinder Singh
Publikováno v: Gut and Liver, Vol 15, Iss 4, Pp 492-499 (2021)
Recent advances in endoscopic imaging of the esophagus have revolutionized the diagnostic capability for detecting premalignant changes and early esophageal malignancy. In this article, we review the practical application of narrow-band imaging focus
Externí odkaz: https://doaj.org/article/19ceefbc78864b309ca1132255d58e41
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2
Artificial intelligence in upper GI endoscopy ‐ current status, challenges and future promise
Autor: Rajvinder Singh, Honggang Yu, Seon Ho Shin, Khek Yu Ho
Publikováno v: Journal of Gastroenterology and Hepatology. 36:20-24
White-light endoscopy with biopsy is the current gold standard modality for detecting and diagnosing upper gastrointestinal (GI) pathology. However, missed lesions remain a challenge. To overcome interobserver variability and learning curve issues, a
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bcbcc1c4f78c0292fedb8984b9d87a7a
https://doi.org/10.1111/jgh.15354
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3
In Defense of Kalman Filtering for Polyp Tracking from Colonoscopy Videos
Autor: David Butler, Yuan Zhang, Tim Chen, Seon Ho Shin, Rajvinder Singh, Gustavo Carneiro
Real-time and robust automatic detection of polyps from colonoscopy videos are essential tasks to help improve the performance of doctors during this exam. The current focus of the field is on the development of accurate but inefficient detectors tha
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ec4146ee3fbdb3828ec6289c63a7c59a
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Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images
Autor: Yuanhong Chen, Yu Tian, Rajvinder Singh, Gustavo Carneiro, Fengbei Liu, Johan W. Verjans, Guansong Pang, Seon Ho Shin
Publikováno v: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 ISBN: 9783030872397
MICCAI (5)
Unsupervised anomaly detection (UAD) learns one-class classifiers exclusively with normal (i.e., healthy) images to detect any abnormal (i.e., unhealthy) samples that do not conform to the expected normal patterns. UAD has two main advantages over it
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::2ed6dcea50bba72f44129457c8152767
https://doi.org/10.1007/978-3-030-87240-3_13
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