Zobrazeno 1 - 10
of 350
pro vyhledávání: '"Yang, Yidong"'
Automatic deep learning segmentation models has been shown to improve both the segmentation efficiency and the accuracy. However, training a robust segmentation model requires considerably large labeled training samples, which may be impractical. Thi
Externí odkaz:
http://arxiv.org/abs/2206.09065
Autor:
Zhang, Jian, Tian, Ruonan, Liu, Jia, Yuan, Jie, Zhang, Siwen, Chi, Zhexu, Yu, Weiwei, Yu, Qianzhou, Wang, Zhen, Chen, Sheng, Li, Mobai, Yang, Dehang, Hu, Tianyi, Deng, Qiqi, Lu, Xiaoyang, Yang, Yidong, Zhou, Rongbin, Zhang, Xue, Liu, Wanlu, Wang, Di
Publikováno v:
In Cell 31 October 2024 187(22):6251-6271
Publikováno v:
In International Journal of Heat and Mass Transfer October 2024 231
Publikováno v:
In Neurocomputing 7 September 2024 597
Publikováno v:
In Annals of Hepatology September-October 2024 29(5)
Autor:
Yang, Yidong
The cell infiltration into the myocardial infarction (MI) site was studied using magnetic resonance imaging (MRI) with micrometer-sized iron oxide particles (MPIO) as cell labeling probes. MI is a leading cause of global death and disability. However
Externí odkaz:
http://hdl.handle.net/1853/41151
Autor:
Liu, Mengqiu, Liu, Ying, Yang, Yidong, Liu, Aiping, Li, Shana, Qu, Changbing, Qiu, Xiaohui, Li, Yang, Lv, Weifu, Zhang, Peng, Wen, Jie
Background: Triage of patients is important to control the pandemic of coronavirus disease 2019 (COVID-19), especially during the peak of the pandemic when clinical resources become extremely limited. Purpose: To develop a method that automatically s
Externí odkaz:
http://arxiv.org/abs/2112.05900
Autor:
Liao, Jiejie1 (AUTHOR), Yang, Yidong2 (AUTHOR), Han, Zhili3,4,5 (AUTHOR), Mo, Lei1 (AUTHOR) molei@m.scnu.edu.cn
Publikováno v:
Behavioral Sciences (2076-328X). Aug2024, Vol. 14 Issue 8, p632. 13p.
Autor:
Fang, Chengyijue, Liu, Yingao, Liu, Mengqiu, Qiu, Xiaohui, Liu, Ying, Li, Yang, Wen, Jie, Yang, Yidong
COVID-19 has become a global pandemic and is still posing a severe health risk to the public. Accurate and efficient segmentation of pneumonia lesions in CT scans is vital for treatment decision-making. We proposed a novel unsupervised approach using
Externí odkaz:
http://arxiv.org/abs/2111.11602
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