Zobrazeno 1 - 10
of 2 050
pro vyhledávání: '"Gui Mei"'
Autor:
Huang, De-Xing, Zhou, Xiao-Hu, Gui, Mei-Jiang, Xie, Xiao-Liang, Liu, Shi-Qi, Wang, Shuang-Yi, Li, Hao, Xiang, Tian-Yu, Hou, Zeng-Guang
Iodinated contrast agents are widely utilized in numerous interventional procedures, yet posing substantial health risks to patients. This paper presents CAS-GAN, a novel GAN framework that serves as a ``virtual contrast agent" to synthesize X-ray an
Externí odkaz:
http://arxiv.org/abs/2410.08490
Autor:
Huang, De-Xing, Zhou, Xiao-Hu, Xie, Xiao-Liang, Liu, Shi-Qi, Wang, Shuang-Yi, Feng, Zhen-Qiu, Gui, Mei-Jiang, Li, Hao, Xiang, Tian-Yu, Yao, Bo-Xian, Hou, Zeng-Guang
Automatic vessel segmentation is paramount for developing next-generation interventional navigation systems. However, current approaches suffer from suboptimal segmentation performances due to significant challenges in intraoperative images (i.e., lo
Externí odkaz:
http://arxiv.org/abs/2406.19749
Autor:
Huang, De-Xing, Zhou, Xiao-Hu, Xie, Xiao-Liang, Liu, Shi-Qi, Feng, Zhen-Qiu, Gui, Mei-Jiang, Li, Hao, Xiang, Tian-Yu, Liu, Xiu-Ling, Hou, Zeng-Guang
Medical image segmentation takes an important position in various clinical applications. Deep learning has emerged as the predominant solution for automated segmentation of volumetric medical images. 2.5D-based segmentation models bridge computationa
Externí odkaz:
http://arxiv.org/abs/2401.11856
Autor:
Liu, Xiao-Yin, Zhou, Xiao-Hu, Li, Guotao, Li, Hao, Gui, Mei-Jiang, Xiang, Tian-Yu, Huang, De-Xing, Hou, Zeng-Guang
Offline reinforcement learning (RL) faces a significant challenge of distribution shift. Model-free offline RL penalizes the Q value for out-of-distribution (OOD) data or constrains the policy closed to the behavior policy to tackle this problem, but
Externí odkaz:
http://arxiv.org/abs/2312.03991
Autor:
Li, Hao, Zhou, Xiao-Hu, Xie, Xiao-Liang, Liu, Shi-Qi, Feng, Zhen-Qiu, Liu, Xiao-Yin, Gui, Mei-Jiang, Xiang, Tian-Yu, Huang, De-Xing, Yao, Bo-Xian, Hou, Zeng-Guang
Offline reinforcement learning (RL) aims to optimize policy using collected data without online interactions. Model-based approaches are particularly appealing for addressing offline RL challenges due to their capability to mitigate the limitations o
Externí odkaz:
http://arxiv.org/abs/2310.17245
Autor:
Liu, Xiao-Yin, Zhou, Xiao-Hu, Gui, Mei-Jiang, Xie, Xiao-Liang, Liu, Shi-Qi, Wang, Shuang-Yi, Li, Hao, Xiang, Tian-Yu, Huang, De-Xing, Hou, Zeng-Guang
Model-based reinforcement learning (RL), which learns environment model from offline dataset and generates more out-of-distribution model data, has become an effective approach to the problem of distribution shift in offline RL. Due to the gap betwee
Externí odkaz:
http://arxiv.org/abs/2309.08925
Autor:
Kang-Bo Huang, Cheng-Peng Gui, Yun-Ze Xu, Xue-Song Li, Hong-Wei Zhao, Jia-Zheng Cao, Yu-Hang Chen, Yi-Hui Pan, Bing Liao, Yun Cao, Xin-Ke Zhang, Hui Han, Fang-Jian Zhou, Ran-Yi Liu, Wen-Fang Chen, Ze-Ying Jiang, Zi-Hao Feng, Fu-Neng Jiang, Yan-Fei Yu, Sheng-Wei Xiong, Guan-Peng Han, Qi Tang, Kui Ouyang, Gui-Mei Qu, Ji-Tao Wu, Ming Cao, Bai-Jun Dong, Yi-Ran Huang, Jin Zhang, Cai-Xia Li, Pei-Xing Li, Wei Chen, Wei-De Zhong, Jian-Ping Guo, Zhi-Ping Liu, Jer-Tsong Hsieh, Dan Xie, Mu-Yan Cai, Wei Xue, Jin-Huan Wei, Jun-Hang Luo
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-12 (2024)
Abstract Integrating genomics and histology for cancer prognosis demonstrates promise. Here, we develop a multi-classifier system integrating a lncRNA-based classifier, a deep learning whole-slide-image-based classifier, and a clinicopathological cla
Externí odkaz:
https://doaj.org/article/c0dc0f9c92514591b5800e5499188a06
Publikováno v:
ZooKeys, Vol 1193, Iss , Pp 111-123 (2024)
A taxonomic revision and redescription of the genus Eurymesosa Breuning, 1938 are presented, including a key to species. Three of the five currently accepted species are considered valid: Eurymesosa ventralis (Pascoe, 1865), Eurymesosa allapsa (Pasco
Externí odkaz:
https://doaj.org/article/7b0277ee41db4c41927b603287a26d3a
Publikováno v:
Teacher Development Research. jun2018, Vol. 2 Issue 2, p102-108. 7p.
Publikováno v:
In Polyhedron 1 October 2024 261