Zobrazeno 1 - 10
of 243
pro vyhledávání: '"XU Mengya"'
Autor:
Xu, Mengya, Mo, Wenjin, Wang, Guankun, Gao, Huxin, Wang, An, Li, Zhen, Yang, Xiaoxiao, Ren, Hongliang
Purpose: Endoscopic surgical environments present challenges for dissection zone segmentation due to unclear boundaries between tissue types, leading to segmentation errors where models misidentify or overlook edges. This study aims to provide precis
Externí odkaz:
http://arxiv.org/abs/2411.18169
Autor:
Xu, Mengya, Mo, Wenjin, Wang, Guankun, Gao, Huxin, Wang, An, Bai, Long, Lyu, Chaoyang, Yang, Xiaoxiao, Li, Zhen, Ren, Hongliang
Robot-assisted Endoscopic Submucosal Dissection (ESD) improves the surgical procedure by providing a more comprehensive view through advanced robotic instruments and bimanual operation, thereby enhancing dissection efficiency and accuracy. Accurate p
Externí odkaz:
http://arxiv.org/abs/2411.18884
Accurate depth perception is crucial for patient outcomes in endoscopic surgery, yet it is compromised by image distortions common in surgical settings. To tackle this issue, our study presents a benchmark for assessing the robustness of endoscopic d
Externí odkaz:
http://arxiv.org/abs/2409.16063
Autor:
Yu, Jieming, Wang, An, Dong, Wenzhen, Xu, Mengya, Islam, Mobarakol, Wang, Jie, Bai, Long, Ren, Hongliang
The recent Segment Anything Model (SAM) 2 has demonstrated remarkable foundational competence in semantic segmentation, with its memory mechanism and mask decoder further addressing challenges in video tracking and object occlusion, thereby achieving
Externí odkaz:
http://arxiv.org/abs/2408.04593
As a crucial and intricate task in robotic minimally invasive surgery, reconstructing surgical scenes using stereo or monocular endoscopic video holds immense potential for clinical applications. NeRF-based techniques have recently garnered attention
Externí odkaz:
http://arxiv.org/abs/2408.04426
Autor:
He, Runlong, Xu, Mengya, Das, Adrito, Khan, Danyal Z., Bano, Sophia, Marcus, Hani J., Stoyanov, Danail, Clarkson, Matthew J., Islam, Mobarakol
Visual Question Answering (VQA) within the surgical domain, utilizing Large Language Models (LLMs), offers a distinct opportunity to improve intra-operative decision-making and facilitate intuitive surgeon-AI interaction. However, the development of
Externí odkaz:
http://arxiv.org/abs/2405.13949
Autor:
Sun, Guodong, Peng, Yuting, Cheng, Le, Xu, Mengya, Wang, An, Wu, Bo, Ren, Hongliang, Zhang, Yang
The precise segmentation of ore images is critical to the successful execution of the beneficiation process. Due to the homogeneous appearance of the ores, which leads to low contrast and unclear boundaries, accurate segmentation becomes challenging,
Externí odkaz:
http://arxiv.org/abs/2402.17370
Deep Neural Networks (DNNs) based semantic segmentation of the robotic instruments and tissues can enhance the precision of surgical activities in robot-assisted surgery. However, in biological learning, DNNs cannot learn incremental tasks over time
Externí odkaz:
http://arxiv.org/abs/2402.05860
Autor:
Huang, Yiming, Cui, Beilei, Bai, Long, Guo, Ziqi, Xu, Mengya, Islam, Mobarakol, Ren, Hongliang
In the realm of robot-assisted minimally invasive surgery, dynamic scene reconstruction can significantly enhance downstream tasks and improve surgical outcomes. Neural Radiance Fields (NeRF)-based methods have recently risen to prominence for their
Externí odkaz:
http://arxiv.org/abs/2401.16416
Autor:
Psychogyios, Dimitrios, Colleoni, Emanuele, Van Amsterdam, Beatrice, Li, Chih-Yang, Huang, Shu-Yu, Li, Yuchong, Jia, Fucang, Zou, Baosheng, Wang, Guotai, Liu, Yang, Boels, Maxence, Huo, Jiayu, Sparks, Rachel, Dasgupta, Prokar, Granados, Alejandro, Ourselin, Sebastien, Xu, Mengya, Wang, An, Wu, Yanan, Bai, Long, Ren, Hongliang, Yamada, Atsushi, Harai, Yuriko, Ishikawa, Yuto, Hayashi, Kazuyuki, Simoens, Jente, DeBacker, Pieter, Cisternino, Francesco, Furnari, Gabriele, Mottrie, Alex, Ferraguti, Federica, Kondo, Satoshi, Kasai, Satoshi, Hirasawa, Kousuke, Kim, Soohee, Lee, Seung Hyun, Lee, Kyu Eun, Kong, Hyoun-Joong, Fu, Kui, Li, Chao, An, Shan, Krell, Stefanie, Bodenstedt, Sebastian, Ayobi, Nicolas, Perez, Alejandra, Rodriguez, Santiago, Puentes, Juanita, Arbelaez, Pablo, Mohareri, Omid, Stoyanov, Danail
Surgical tool segmentation and action recognition are fundamental building blocks in many computer-assisted intervention applications, ranging from surgical skills assessment to decision support systems. Nowadays, learning-based action recognition an
Externí odkaz:
http://arxiv.org/abs/2401.00496