Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Wanfang Xie"'
Autor:
Xun Zhao, Ye Yan, Wanfang Xie, Zijian Qin, Litao Zhao, Cheng Liu, Shudong Zhang, Jiangang Liu, Lulin Ma
Publikováno v:
BMC Cancer, Vol 24, Iss 1, Pp 1-10 (2024)
Abstract Rationale and Objectives The management of complex renal cysts is guided by the Bosniak classification system, which may be inadequate for risk stratification of patients to determine the appropriate intervention. Radiomics models based on C
Externí odkaz:
https://doaj.org/article/577207fe0ee740d3b8638af548305d79
Publikováno v:
IEEE Transactions on Instrumentation and Measurement. 70:1-8
Localization accuracy is a fundamental requirement for Simultaneous Localization and Mapping (SLAM) systems. Traditional visual SLAM (vSLAM) schemes are usually based upon the assumption of static environments, so they do not perform well in dynamic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d88848c6ad62092cf0e5ad838d5387fe
https://doi.org/10.1109/tim.2020.3026803
https://doi.org/10.1109/tim.2020.3026803
Autor:
Litao Zhao, Jie Bao, Xiaomeng Qiao, Pengfei Jin, Yanting Ji, Zhenkai Li, Ji Zhang, Yueting Su, Libiao Ji, Junkang Shen, Yueyue Zhang, Lei Niu, Wanfang Xie, Chunhong Hu, Hailin Shen, Ximing Wang, Jiangang Liu, Jie Tian
Purpose: To construct deep learning (DL) models based on multicentre biparametric MRI (bpMRI) for the diagnosis of clinically significant prostate cancer (csPCa), and compare the performance of these models with that of Prostate Imaging Reporting and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::420bab27118c4d132365626bfebb5086
https://doi.org/10.21203/rs.3.rs-1570686/v1
https://doi.org/10.21203/rs.3.rs-1570686/v1
Autor:
Litao Zhao, Jie Bao, Xiaomeng Qiao, Pengfei Jin, Yanting Ji, Zhenkai Li, Ji Zhang, Yueting Su, Libiao Ji, Junkang Shen, Yueyue Zhang, Lei Niu, Wanfang Xie, Chunhong Hu, Hailin Shen, Ximing Wang, Jiangang Liu, Jie Tian
Publikováno v:
European journal of nuclear medicine and molecular imaging.
Purpose This study aimed to develop deep learning (DL) models based on multicentre biparametric magnetic resonance imaging (bpMRI) for the diagnosis of clinically significant prostate cancer (csPCa) and compare the performance of these models with th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1d5550d4ede7e9fed546469fd211e7c6
https://pubmed.ncbi.nlm.nih.gov/36409317
https://pubmed.ncbi.nlm.nih.gov/36409317