Zobrazeno 1 - 10
of 667
pro vyhledávání: '"Zheng, Guoyan"'
Autor:
Song, Zhiyun, Zhao, Yinjie, Li, Xiaomin, Fei, Manman, Zhao, Xiangyu, Liu, Mengjun, Chen, Cunjian, Yeh, Chung-Hsing, Wang, Qian, Zheng, Guoyan, Ai, Songtao, Zhang, Lichi
High-resolution (HR) 3D magnetic resonance imaging (MRI) can provide detailed anatomical structural information, enabling precise segmentation of regions of interest for various medical image analysis tasks. Due to the high demands of acquisition dev
Externí odkaz:
http://arxiv.org/abs/2410.10097
Autor:
Das, Adrito, Khan, Danyal Z., Psychogyios, Dimitrios, Zhang, Yitong, Hanrahan, John G., Vasconcelos, Francisco, Pang, You, Chen, Zhen, Wu, Jinlin, Zou, Xiaoyang, Zheng, Guoyan, Qayyum, Abdul, Mazher, Moona, Razzak, Imran, Li, Tianbin, Ye, Jin, He, Junjun, Płotka, Szymon, Kaleta, Joanna, Yamlahi, Amine, Jund, Antoine, Godau, Patrick, Kondo, Satoshi, Kasai, Satoshi, Hirasawa, Kousuke, Rivoir, Dominik, Pérez, Alejandra, Rodriguez, Santiago, Arbeláez, Pablo, Stoyanov, Danail, Marcus, Hani J., Bano, Sophia
The field of computer vision applied to videos of minimally invasive surgery is ever-growing. Workflow recognition pertains to the automated recognition of various aspects of a surgery: including which surgical steps are performed; and which surgical
Externí odkaz:
http://arxiv.org/abs/2409.01184
Autor:
Huang, Baoru, Vo, Tuan, Kongtongvattana, Chayun, Dagnino, Giulio, Kundrat, Dennis, Chi, Wenqiang, Abdelaziz, Mohamed, Kwok, Trevor, Jianu, Tudor, Do, Tuong, Le, Hieu, Nguyen, Minh, Nguyen, Hoan, Tjiputra, Erman, Tran, Quang, Xie, Jianyang, Meng, Yanda, Bhattarai, Binod, Tan, Zhaorui, Liu, Hongbin, Gan, Hong Seng, Wang, Wei, Yang, Xi, Wang, Qiufeng, Su, Jionglong, Huang, Kaizhu, Stefanidis, Angelos, Guo, Min, Du, Bo, Tao, Rong, Vu, Minh, Zheng, Guoyan, Zheng, Yalin, Vasconcelos, Francisco, Stoyanov, Danail, Elson, Daniel, Baena, Ferdinando Rodriguez y, Nguyen, Anh
Real-time visual feedback from catheterization analysis is crucial for enhancing surgical safety and efficiency during endovascular interventions. However, existing datasets are often limited to specific tasks, small scale, and lack the comprehensive
Externí odkaz:
http://arxiv.org/abs/2408.13126
Autor:
Liu, Peng, Zheng, Guoyan
Semi-supervised learning (SSL) methods, which can leverage a large amount of unlabeled data for improved performance, has attracted increasing attention recently. In this paper, we introduce a novel Context-aware Conditional Cross Pseudo Supervision
Externí odkaz:
http://arxiv.org/abs/2306.08275
Autor:
Nwoye, Chinedu Innocent, Yu, Tong, Sharma, Saurav, Murali, Aditya, Alapatt, Deepak, Vardazaryan, Armine, Yuan, Kun, Hajek, Jonas, Reiter, Wolfgang, Yamlahi, Amine, Smidt, Finn-Henri, Zou, Xiaoyang, Zheng, Guoyan, Oliveira, Bruno, Torres, Helena R., Kondo, Satoshi, Kasai, Satoshi, Holm, Felix, Özsoy, Ege, Gui, Shuangchun, Li, Han, Raviteja, Sista, Sathish, Rachana, Poudel, Pranav, Bhattarai, Binod, Wang, Ziheng, Rui, Guo, Schellenberg, Melanie, Vilaça, João L., Czempiel, Tobias, Wang, Zhenkun, Sheet, Debdoot, Thapa, Shrawan Kumar, Berniker, Max, Godau, Patrick, Morais, Pedro, Regmi, Sudarshan, Tran, Thuy Nuong, Fonseca, Jaime, Nölke, Jan-Hinrich, Lima, Estevão, Vazquez, Eduard, Maier-Hein, Lena, Navab, Nassir, Mascagni, Pietro, Seeliger, Barbara, Gonzalez, Cristians, Mutter, Didier, Padoy, Nicolas
Publikováno v:
Medical Image Analysis, Volume 89, 2023, 102888, ISSN 1361-8415
Formalizing surgical activities as triplets of the used instruments, actions performed, and target anatomies is becoming a gold standard approach for surgical activity modeling. The benefit is that this formalization helps to obtain a more detailed u
Externí odkaz:
http://arxiv.org/abs/2302.06294
Phase recognition plays an essential role for surgical workflow analysis in computer assisted intervention. Transformer, originally proposed for sequential data modeling in natural language processing, has been successfully applied to surgical phase
Externí odkaz:
http://arxiv.org/abs/2209.01148
Three-dimensional (3D) integrated renal structures (IRS) segmentation is important in clinical practice. With the advancement of deep learning techniques, many powerful frameworks focusing on medical image segmentation are proposed. In this challenge
Externí odkaz:
http://arxiv.org/abs/2208.05772