Zobrazeno 1 - 10
of 172
pro vyhledávání: '"Xu, Mengya"'
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
Accurate and robust medical image segmentation is fundamental and crucial for enhancing the autonomy of computer-aided diagnosis and intervention systems. Medical data collection normally involves different scanners, protocols, and populations, makin
Externí odkaz:
http://arxiv.org/abs/2306.03511
Fully-supervised polyp segmentation has accomplished significant triumphs over the years in advancing the early diagnosis of colorectal cancer. However, label-efficient solutions from weak supervision like scribbles are rarely explored yet primarily
Externí odkaz:
http://arxiv.org/abs/2306.00451