Quantitative texture analysis using machine learning for predicting interpretable pulmonary perfusion from non-contrast computed tomography in pulmonary embolism patients

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Publication Details

Journal
Respiratory Research
Published
2024-10-28
DOI
https://doi.org/10.1186/s12931-024-03004-9
Citations
8
Primary Topic
Venous Thromboembolism Diagnosis and Management
Type
article
Field-Weighted Citation Impact
4.39

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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 et al.
8 citations
Respiratory Research
Venous Thromboembolism Diagnosis and Management
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.
Respiratory ResearchVol. 25(1)
Hong Kong Polytechnic University (HK), Shenzhen Polytechnic University (CN), Peking University (CN), Peking University Third Hospital (CN), Beihang University (CN)
Health and Medical Research Fund
Zero hunger
Openalex Percentile: Top 5%
Venous Thromboembolism Diagnosis and Management
4.39
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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