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of 39
pro vyhledávání: '"Shin, Seon Ho"'
AI-assisted colonoscopy has received lots of attention in the last decade. Several randomised clinical trials in the previous two years showed exciting results of the improving detection rate of polyps. However, current commercial AI-assisted colonos
Externí odkaz:
http://arxiv.org/abs/2208.02523
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:
http://arxiv.org/abs/2201.11450
Autor:
Tian, Yu, Pang, Guansong, Liu, Fengbei, chen, Yuanhong, Shin, Seon Ho, Verjans, Johan W., Singh, Rajvinder, Carneiro, Gustavo
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:
http://arxiv.org/abs/2103.03423
Autor:
Tian, Yu, Pu, Leonardo Zorron Cheng Tao, Liu, Yuyuan, Maicas, Gabriel, Verjans, Johan W., Burt, Alastair D., Shin, Seon Ho, Singh, Rajvinder, Carneiro, Gustavo
In this paper, we propose and analyse a system that can automatically detect, localise and classify polyps from colonoscopy videos. The detection of frames with polyps is formulated as a few-shot anomaly classification problem, where the training set
Externí odkaz:
http://arxiv.org/abs/2101.03285
Autor:
Tian, Yu, Zorron Cheng Tao Pu, Leonardo, Liu, Yuyuan, Maicas, Gabriel, Verjans, Johan W., Burt, Alastair D., Shin, Seon Ho, Singh, Rajvinder, Carneiro, Gustavo
Publikováno v:
In Deep Learning for Medical Image Analysis Edition: Second Edition. 2024:425-450
Publikováno v:
In Journal of Parallel and Distributed Computing February 2014 74(2):2039-2047
Autor:
Bian, Cheng, Burt, Alastair D., Cao, Xiaohuan, Carass, Aaron, Carneiro, Gustavo, Cha, Kenny H., Chen, Yang, Dong, Qinglin, Duncan, James, Dvornek, Nicha, Fan, Jingfan, Fu, Huazhu, Gao, Yue, Ge, Bao, Gossmann, Alexej, Han, Shuo, Hayat, Munawar, He, Mengshen, He, Yufan, Hu, Xintao, Huang, Heng, Huang, Qiu, Jeon, Eunjin, Ji, Shuyi, Jiang, Xi, Khan, Fahad Shahbaz, Khan, Muhammad Haris, Khan, Salman, Ko, Wonjun, Le, Ngan, Lei, Jianqin, Li, Lei, Li, Qing, Li, Xiaoxiao, Li, Yuexiang, Liang, Dong, Liu, Dingkun, Liu, Luyan, Liu, Tianming, Liu, Yihao, Liu, Yiheng, Liu, Yuyuan, Luu, Khoa, Ma, Kai, Maicas, Gabriel, Mulyadi, Ahmad Wisnu, Nguyen, Hien, Oh, Gyutaek, Petrick, Nicholas, Prince, Jerry L., Qiang, Ning, Quinn, Kyle, Roth, Holger R., Sahiner, Berkman, Samala, Ravi K., Shamshad, Fahad, Shen, Dinggang, Shin, Seon Ho, Singh, Rajvinder, Staib, Lawrence H., Suk, Heung-Il, Sun, Kaicong, Tian, Yu, Tran, Minh, Ventola, Pamela, Verjans, Johan W., Vo-Ho, Viet-Khoa, Wang, Ge, Wang, Han, Wang, Jiyao, Wang, Qiyuan, Wang, Sihang, Wang, Xiaosong, Wen, Si, Wu, Fuping, Wu, Zihao, Xu, Daguang, Xu, Steven, Xu, Ziyue, Xue, Peng, Xue, Zhong, Yang, Dong, Ye, Jong Chul, Yoon, Jee Seok, Zamir, Syed Waqas, Zhang, Lu, Zhang, Wei, Zhao, Jun, Zhao, Lin, Zhao, Shijie, Zheng, Yefeng, Zhou, S. Kevin, Zhu, Dajiang, Zhuang, Juntang, Zhuang, Xiahai, Zorron Cheng Tao Pu, Leonardo, Zuo, Lianrui
Publikováno v:
In Deep Learning for Medical Image Analysis Edition: Second Edition. 2024:xv-xxii
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