Quantitative texture analysis using machine learning for predicting interpretable pulmonary perfusion from non-contrast computed tomography in pulmonary embolism patients
Authors
Zhichun Li
Jing Yan Cai (ORCID: https://orcid.org/0000-0001-6934-0108)
Ge Ren (ORCID: https://orcid.org/0000-0003-2049-2682)
Ruijie Yang (ORCID: https://orcid.org/0000-0002-3459-0075)
Martin Ho Yin Yeung (ORCID: https://orcid.org/0000-0001-8063-2929)
Meng Wang (ORCID: https://orcid.org/0000-0002-4970-8590)
Quantitative texture analysis using machine learning for predicting interpretable pulmonary perfusion from non-contrast computed tomography in pulmonary embolism patients
Zhichun Li, Jing Yan Cai, Ge Ren, Ruijie Yang et al.
Citations
8 citations
Journal
Respiratory Research
Topics
Venous Thromboembolism Diagnosis and Management
Field-Weighted Citation Impact (FWCI)
4.39
article
Quantitative texture analysis using machine learning for predicting interpretable pulmonary perfusion from non-contrast computed tomography in pulmonary embolism patients
Zhichun Li, Jing Yan Cai, Ge Ren, Ruijie Yang, Martin Ho Yin Yeung, Meng Wang, Zhi Chen, Kun Wang, Yuhua Huang, Pu Yao, Xiancong Liu, Meixin Zhao, Zihan Li, Mayang Zhao, Lisheng Geng, Weifang Zhang
article en
8 citations
Abstract
No abstract available for this paper.
Publication Tag
Respiratory ResearchVol. 25(1)
Institutions
Hong Kong Polytechnic University (HK), Shenzhen Polytechnic University (CN), Peking University (CN), Peking University Third Hospital (CN), Beihang University (CN)
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Quantitative texture analysis using machine learning for predicting interpretable pulmonary perfusion from non-contrast computed tomography in pulmonary embolism patients — Zhichun Li, Jing Yan Cai, et al. · Respiratory Research (2024) | TGRS Research Map | TGRS