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pro vyhledávání: '"SINGH, RAJVINDER"'
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
Autor:
Tian, Yu, Pang, Guansong, Liu, Yuyuan, Wang, Chong, Chen, Yuanhong, Liu, Fengbei, Singh, Rajvinder, Verjans, Johan W, Wang, Mengyu, Carneiro, Gustavo
Unsupervised anomaly detection (UAD) aims to find anomalous images by optimising a detector using a training set that contains only normal images. UAD approaches can be based on reconstruction methods, self-supervised approaches, and Imagenet pre-tra
Externí odkaz:
http://arxiv.org/abs/2203.11725
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:
Khalaf, Kareem, Seleq, Samir, Bourke, Michael J., Alkandari, Asma, Bapaye, Amol, Bechara, Robert, Calo, Natalia C., Fedorov, Evgeniy D., Hassan, Cesare, Kalauz, Mirjana, Kandel, Gabor P., Matsuda, Takahisa, May, Gary R., Mönkemüller, Klaus, Mosko, Jeffrey D., Ohno, Akiko, Pavic, Tajana, Pellisé, Maria, Raos, Zoe, Repici, Alessandro, Rex, Douglas K., Saxena, Payal, Schauer, Cameron, Sethi, Amrita, Sharma, Prateek, Shaukat, Aasma, Siddiqui, Uzma D., Singh, Rajvinder, Smith, Lesley-Ann, Tanabe, Mayo, Teshima, Christopher W., von Renteln, Daniel, Gimpaya, Nikko, Pawlak, Katarzyna M., Angeli Fujiyoshi, Mary Raina, Fujiyoshi, Yusuke, Lamba, Mehul, Li, Suqing, Malipatil, Sharan B., Grover, Samir C.
Publikováno v:
In Gastrointestinal Endoscopy September 2024 100(3):510-516
Autor:
Tian, Yu, Liu, Fengbei, Pang, Guansong, Chen, Yuanhong, Liu, Yuyuan, Verjans, Johan W., Singh, Rajvinder, Carneiro, Gustavo
Unsupervised anomaly detection (UAD) methods are trained with normal (or healthy) images only, but during testing, they are able to classify normal and abnormal (or disease) images. UAD is an important medical image analysis (MIA) method to be applie
Externí odkaz:
http://arxiv.org/abs/2109.01303
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:
Tan, Jin Lin, Heng, Kingjin, Chinnaratha, Mohamed Asif, Bulamu, Norma B., Kaambwa, Billingsley, Singh, Rajvinder
Publikováno v:
In iGIE March 2024 3(1):92-103
Autor:
Young, Edward, Rajagopalan, Arvind, Tee, Derrick, Sathananthan, Dharshan, Hoile, Sophie, Singh, Rajvinder
Publikováno v:
In Gastroenterology February 2024 166(2):338-340
Autor:
Tian, Yu, Pang, Guansong, Chen, Yuanhong, Singh, Rajvinder, Verjans, Johan W., Carneiro, Gustavo
Anomaly detection with weakly supervised video-level labels is typically formulated as a multiple instance learning (MIL) problem, in which we aim to identify snippets containing abnormal events, with each video represented as a bag of video snippets
Externí odkaz:
http://arxiv.org/abs/2101.10030
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