Zobrazeno 1 - 10
of 12 334
pro vyhledávání: '"Liang, DONG"'
Autor:
Zhao, Haotian, Chen, Ruifeng, Yan, Jing, Feng, Juan, Xiang, Jun, Chen, Yang, Liang, Dong, Li, Yinsheng
To shorten the door-to-puncture time for better treating patients with acute ischemic stroke, it is highly desired to obtain quantitative cerebral perfusion images using C-arm cone-beam computed tomography (CBCT) equipped in the interventional suite.
Externí odkaz:
http://arxiv.org/abs/2412.05084
Autor:
Wang, Yue, Zhou, Tian, Cui, Zhuo-xu, Huang, Bingsheng, Zheng, Hairong, Liang, Dong, Zhu, Yanjie
Magnetic Resonance Imaging (MRI) is a multi-contrast imaging technique in which different contrast images share similar structural information. However, conventional diffusion models struggle to effectively leverage this structural similarity. Recent
Externí odkaz:
http://arxiv.org/abs/2411.14269
Diffusion model-based approaches recently achieved re-markable success in MRI reconstruction, but integration into clinical routine remains challenging due to its time-consuming convergence. This phenomenon is partic-ularly notable when directly appl
Externí odkaz:
http://arxiv.org/abs/2411.03758
Autor:
Guan, Yu, Zhang, Kunlong, Qi, Qi, Wang, Dong, Ke, Ziwen, Wang, Shaoyu, Liang, Dong, Liu, Qiegen
Diffusion models have recently demonstrated considerable advancement in the generation and reconstruction of magnetic resonance imaging (MRI) data. These models exhibit great potential in handling unsampled data and reducing noise, highlighting their
Externí odkaz:
http://arxiv.org/abs/2411.03723
Meta learning is a promising paradigm in the era of large models and task distributional robustness has become an indispensable consideration in real-world scenarios. Recent advances have examined the effectiveness of tail task risk minimization in f
Externí odkaz:
http://arxiv.org/abs/2410.22788
Autor:
Gu, Shuhao, Zhang, Jialing, Zhou, Siyuan, Yu, Kevin, Xing, Zhaohu, Wang, Liangdong, Cao, Zhou, Jia, Jintao, Zhang, Zhuoyi, Wang, Yixuan, Hu, Zhenchong, Zhang, Bo-Wen, Li, Jijie, Liang, Dong, Zhao, Yingli, Ao, Yulong, Liu, Yaoqi, Feng, Fangxiang, Liu, Guang
Vision-Language Models (VLMs) have recently made significant progress, but the limited scale and quality of open-source instruction data hinder their performance compared to closed-source models. In this work, we address this limitation by introducin
Externí odkaz:
http://arxiv.org/abs/2410.18558
Cardiac T1 mapping can evaluate various clinical symptoms of myocardial tissue. However, there is currently a lack of effective, robust, and efficient methods for motion correction in cardiac T1 mapping. In this paper, we propose a deep learning-base
Externí odkaz:
http://arxiv.org/abs/2410.11651
Autor:
Zhou, Shuo, Zhou, Yihang, Liu, Congcong, Zhu, Yanjie, Zheng, Hairong, Liang, Dong, Wang, Haifeng
Magnetic resonance image reconstruction starting from undersampled k-space data requires the recovery of many potential nonlinear features, which is very difficult for algorithms to recover these features. In recent years, the development of quantum
Externí odkaz:
http://arxiv.org/abs/2410.09406
Autor:
Gong, Chaoguang, Hu, Yue, Li, Peng, Zou, Lixian, Liu, Congcong, Zhou, Yihang, Zhu, Yanjie, Liang, Dong, Wang, Haifeng
Magnetic Resonance Fingerprinting (MRF) has emerged as a promising quantitative imaging technique within the field of Magnetic Resonance Imaging (MRI), offers comprehensive insights into tissue properties by simultaneously acquiring multiple tissue p
Externí odkaz:
http://arxiv.org/abs/2410.06624
Autor:
Zhou, Fang, Huang, Yaning, Liang, Dong, Li, Dai, Zhang, Zhongke, Wang, Kai, Xin, Xiao, Aboelela, Abdallah, Jiang, Zheliang, Wang, Yang, Song, Jeff, Zhang, Wei, Liang, Chen, Li, Huayu, Sun, ChongLin, Yang, Hang, Qu, Lei, Shu, Zhan, Yuan, Mindi, Maccherani, Emanuele, Hayat, Taha, Guo, John, Puvvada, Varna, Pashkevich, Uladzimir
The increasing complexity of deep learning models used for calculating user representations presents significant challenges, particularly with limited computational resources and strict service-level agreements (SLAs). Previous research efforts have
Externí odkaz:
http://arxiv.org/abs/2410.06497