Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Fu, Yan"'
Autor:
He X; School of Information Sciences and Technology, Northwest University, Xi'an, 710127, Shaanxi Province, People's Republic of China., Li K; Department of Ultrasound, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, Province Guangdong, People's Republic of China., Wei R; Department of Interventional Radiology and Vascular Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, Province Guangdong, People's Republic of China., Zuo M; Department of Minimal Invasive Intervention, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 651, Dongfeng East Road, Guangzhou, 510060, People's Republic of China., Yao W; Department of Interventional Radiology and Vascular Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, Province Guangdong, People's Republic of China., Zheng Z; Department of Interventional Therapy, Guangdong Provincial Hospital of Chinese, Medicine and Guangdong Provincial Academy of Chinese Medical Sciences, No. 111 Dade Road, Guangzhou, 510080, Guangdong, People's Republic of China., He X; Department of Interventional Therapy, Guangdong Provincial Hospital of Chinese, Medicine and Guangdong Provincial Academy of Chinese Medical Sciences, No. 111 Dade Road, Guangzhou, 510080, Guangdong, People's Republic of China., Fu Y; Department of Interventional Therapy, National Cancer Center/National Clinical, Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical, Sciences and Peking Union Medical College, Beijing, 100021, People's Republic of China., Li C; Department of Interventional Radiology and Vascular Surgery, The First Affiliated Hospital of Jinan University, Guangzhou, 510060, People's Republic of China. lichengzhi@jnu.edu.cn., An C; Department of Minimal Invasive Intervention, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 651, Dongfeng East Road, Guangzhou, 510060, People's Republic of China. anchao-1983@163.com., Liu W; Department of Interventional Therapy, Guangdong Provincial Hospital of Chinese, Medicine and Guangdong Provincial Academy of Chinese Medical Sciences, No. 111 Dade Road, Guangzhou, 510080, Guangdong, People's Republic of China. Liu_wendao_2023@163.com.
Publikováno v:
La Radiologia medica [Radiol Med] 2023 Dec; Vol. 128 (12), pp. 1508-1520. Date of Electronic Publication: 2023 Oct 06.
Autor:
Yang J; CEMS, NCMIS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.; School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China., Cheng Z; CEMS, NCMIS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.; School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China., Gong F; CEMS, NCMIS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.; School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China., Fu Y; CEMS, NCMIS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.; School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
Publikováno v:
Analytical chemistry [Anal Chem] 2023 Apr 18; Vol. 95 (15), pp. 6235-6243. Date of Electronic Publication: 2023 Mar 12.
Autor:
Li W; The First Construction Engineering Limited Company of China Construction Third Engineering Bureau, Wuhan, P.R. China., Cheng J; The First Construction Engineering Limited Company of China Construction Third Engineering Bureau, Wuhan, P.R. China., Chen B; The First Construction Engineering Limited Company of China Construction Third Engineering Bureau, Wuhan, P.R. China., Xue Y; The First Construction Engineering Limited Company of China Construction Third Engineering Bureau, Wuhan, P.R. China., Wang Y; Sichuan University of Science & Engineering, Yibin, P.R. China., Fu Y; School of Computer Science & Engineering, University of Electronic Science and Technology of China, Chengdu, P.R. China.; Chengdu Union Big Data Tech. Inc., Chengdu, P.R. China., Zhou J; School of Computer Science & Engineering, University of Electronic Science and Technology of China, Chengdu, P.R. China.; Chengdu Union Big Data Tech. Inc., Chengdu, P.R. China., Chen D; School of Computer Science & Engineering, University of Electronic Science and Technology of China, Chengdu, P.R. China.; Chengdu Union Big Data Tech. Inc., Chengdu, P.R. China.
Publikováno v:
PloS one [PLoS One] 2023 Jan 26; Vol. 18 (1), pp. e0271051. Date of Electronic Publication: 2023 Jan 26 (Print Publication: 2023).
Autor:
An C; Department of Minimal Invasive Intervention, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China.; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, 100191, China., Li D; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, 100191, China.; CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China., Li S; Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China., Li W; Department of Nasopharyngeal Carcinoma, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China., Tong T; CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China., Liu L; Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China., Jiang D; Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China., Jiang L; Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China., Ruan G; Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China., Hai N; Department of Ultrasound, Beijing Chao Yang Hospital, Capital Medical University, Beijing, 100010, China., Fu Y; Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China., Wang K; CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. kun.wang@ia.ac.cn.; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China. kun.wang@ia.ac.cn., Zhuo S; Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China. zhuosq@sysucc.edu.cn., Tian J; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, 100191, China. jie.tian@ia.ac.cn.; CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. jie.tian@ia.ac.cn.; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China. jie.tian@ia.ac.cn.
Publikováno v:
European journal of nuclear medicine and molecular imaging [Eur J Nucl Med Mol Imaging] 2022 Mar; Vol. 49 (4), pp. 1187-1199. Date of Electronic Publication: 2021 Oct 15.
Autor:
Yang J; CEMS, NCMIS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, P. R. China.; School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, P. R. China., Gao Z; CEMS, NCMIS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, P. R. China.; School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, P. R. China., Ren X; School of Sciences, China University of Mining & Technology, Beijing 100083, P. R. China., Sheng J; State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, P. R. China., Xu P; State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, P. R. China., Chang C; State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, P. R. China., Fu Y; CEMS, NCMIS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, P. R. China.; School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, P. R. China.
Publikováno v:
Analytical chemistry [Anal Chem] 2021 Apr 20; Vol. 93 (15), pp. 6094-6103. Date of Electronic Publication: 2021 Apr 07.
Autor:
Chen X; Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou 510180, China., Zeng M; Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou 510180, China., Tong Y; Sun Yat-sen University, Guangzhou, China., Zhang T; Philips Healthcare, Guangzhou, China., Fu Y; EPFL, Lausanne, Switzerland., Li H; Sun Yat-sen University, Guangzhou, China., Zhang Z; Philips Healthcare, Guangzhou, China., Cheng Z; Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou 510180, China., Xu X; Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou 510180, China., Yang R; Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou 510180, China., Liu Z; Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China., Wei X; Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou 510180, China., Jiang X; Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou 510180, China.
Publikováno v:
BioMed research international [Biomed Res Int] 2020 Sep 23; Vol. 2020, pp. 9258649. Date of Electronic Publication: 2020 Sep 23 (Print Publication: 2020).
Publikováno v:
Xi'an Gongcheng Daxue xuebao, Vol 37, Iss 4, Pp 118-126 (2023)
The fires can be effectively detected by monitoring fire smoke. However, existing fire smoke segmentation algorithms have not performed well on the small smoke and edges of large smoke. This article proposed an improved lightweight DeepLabV3+ smoke s
Externí odkaz:
https://doaj.org/article/20b5ca55ddcf4da58e991eddcbccb1a3
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
Jisuanji kexue yu tansuo, Vol 16, Iss 9, Pp 1933-1953 (2022)
The current scheme of manually extracting features for object detection has been replaced by deep learning. Deep learning technology has greatly promoted the development of object detection technology. Object detection has also become one of the most
Externí odkaz:
https://doaj.org/article/f700376035d44dea82731b7acaebc887
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.