Zobrazeno 1 - 10
of 196
pro vyhledávání: '"Xu, Mengya"'
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
The Segment Anything Model (SAM) serves as a fundamental model for semantic segmentation and demonstrates remarkable generalization capabilities across a wide range of downstream scenarios. In this empirical study, we examine SAM's robustness and zer
Externí odkaz:
http://arxiv.org/abs/2308.07156
Noisy label problems are inevitably in existence within medical image segmentation causing severe performance degradation. Previous segmentation methods for noisy label problems only utilize a single image while the potential of leveraging the correl
Externí odkaz:
http://arxiv.org/abs/2307.05898
Despite their impressive performance in various surgical scene understanding tasks, deep learning-based methods are frequently hindered from deploying to real-world surgical applications for various causes. Particularly, data collection, annotation,
Externí odkaz:
http://arxiv.org/abs/2306.16285