Zobrazeno 1 - 10
of 264
pro vyhledávání: '"Wang, Yipei"'
Autor:
Yi, Weixi, Wang, Yipei, Thorley, Natasha, Ng, Alexander, Punwani, Shonit, Kasivisvanathan, Veeru, Barratt, Dean C., Saeed, Shaheer Ullah, Hu, Yipeng
Current imaging-based prostate cancer diagnosis requires both MR T2-weighted (T2w) and diffusion-weighted imaging (DWI) sequences, with additional sequences for potentially greater accuracy improvement. However, measuring diffusion patterns in DWI se
Externí odkaz:
http://arxiv.org/abs/2411.07416
Autor:
Wu, Xiangcen, Wang, Yipei, Yang, Qianye, Thorley, Natasha, Punwani, Shonit, Kasivisvanathan, Veeru, Bonmati, Ester, Hu, Yipeng
Prostate cancer diagnosis through MR imaging have currently relied on radiologists' interpretation, whilst modern AI-based methods have been developed to detect clinically significant cancers independent of radiologists. In this study, we propose to
Externí odkaz:
http://arxiv.org/abs/2410.23084
The capabilities of large language models (LLMs) have been applied in expert systems across various domains, providing new opportunities for AI in Education. Educational interactions involve a cyclical exchange between teachers and students. Current
Externí odkaz:
http://arxiv.org/abs/2410.15701
Large vision-language models (LVLMs) have made significant strides in addressing complex video tasks, sparking researchers' interest in their human-like multimodal understanding capabilities. Video description serves as a fundamental task for evaluat
Externí odkaz:
http://arxiv.org/abs/2410.15270
Autor:
Xu, Yinsong, Wang, Yipei, Shen, Ziyi, Gayo, Iani J. M. B., Thorley, Natasha, Punwani, Shonit, Men, Aidong, Barratt, Dean, Chen, Qingchao, Hu, Yipeng
The Gleason groups serve as the primary histological grading system for prostate cancer, providing crucial insights into the cancer's potential for growth and metastasis. In clinical practice, pathologists determine the Gleason groups based on specim
Externí odkaz:
http://arxiv.org/abs/2407.05796
Autor:
Jia, Jian, Wang, Yipei, Li, Yan, Chen, Honggang, Bai, Xuehan, Liu, Zhaocheng, Liang, Jian, Chen, Quan, Li, Han, Jiang, Peng, Gai, Kun
Contemporary recommender systems predominantly rely on collaborative filtering techniques, employing ID-embedding to capture latent associations among users and items. However, this approach overlooks the wealth of semantic information embedded withi
Externí odkaz:
http://arxiv.org/abs/2405.03988
Alzheimer's disease (AD) is a progressive and irreversible brain disorder that unfolds over the course of 30 years. Therefore, it is critical to capture the disease progression in an early stage such that intervention can be applied before the onset
Externí odkaz:
http://arxiv.org/abs/2403.06087
Autor:
Li, Yiwen, Fu, Yunguan, Gayo, Iani J. M. B., Yang, Qianye, Min, Zhe, Saeed, Shaheer U., Yan, Wen, Wang, Yipei, Noble, J. Alison, Emberton, Mark, Clarkson, Matthew J., Barratt, Dean C., Prisacariu, Victor A., Hu, Yipeng
For training registration networks, weak supervision from segmented corresponding regions-of-interest (ROIs) have been proven effective for (a) supplementing unsupervised methods, and (b) being used independently in registration tasks in which unsupe
Externí odkaz:
http://arxiv.org/abs/2402.10728
Autor:
Zhao, Xin, Hu, Shiyu, Wang, Yipei, Zhang, Jing, Hu, Yimin, Liu, Rongshuai, Ling, Haibin, Li, Yin, Li, Renshu, Liu, Kun, Li, Jiadong
Publikováno v:
Int J Comput Vis (2023)
Single object tracking (SOT) is a fundamental problem in computer vision, with a wide range of applications, including autonomous driving, augmented reality, and robot navigation. The robustness of SOT faces two main challenges: tiny target and fast
Externí odkaz:
http://arxiv.org/abs/2402.04519
Autor:
Li, Yiwen, Fu, Yunguan, Gayo, Iani, Yang, Qianye, Min, Zhe, Saeed, Shaheer, Yan, Wen, Wang, Yipei, Noble, J. Alison, Emberton, Mark, Clarkson, Matthew J., Huisman, Henkjan, Barratt, Dean, Prisacariu, Victor Adrian, Hu, Yipeng
The prowess that makes few-shot learning desirable in medical image analysis is the efficient use of the support image data, which are labelled to classify or segment new classes, a task that otherwise requires substantially more training images and
Externí odkaz:
http://arxiv.org/abs/2209.05160