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Yunfei Zha
Researcher at Wuhan University
Publications - 28
Citations - 2802
Yunfei Zha is an academic researcher from Wuhan University. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 11, co-authored 16 publications receiving 1363 citations.
Papers
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Journal ArticleDOI
Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography.
Kang Zhang,Xiaohong Liu,Jun Shen,Zhihuan Li,Ye Sang,Xingwang Wu,Yunfei Zha,Wenhua Liang,Chengdi Wang,Ke Wang,Linsen Ye,Ming Gao,Zhongguo Zhou,Liang Li,Jin Wang,Zehong Yang,Huimin Cai,Jie Xu,Lei Yang,Wenjia Cai,Wenqin Xu,Shaoxu Wu,Wei Zhang,Shanping Jiang,Lianghong Zheng,Xuan Zhang,Li Wang,Liu Lu,Jiaming Li,Haiping Yin,Winston Wang,Oulan Li,Charlotte Zhang,Liang Liang,Tao Wu,Ruiyun Deng,Kang Wei,Yong Zhou,Ting Chen,Johnson Y.N. Lau,Manson Fok,Jianxing He,Tianxin Lin,Weimin Li,Guangyu Wang +44 more
TL;DR: Using a large computed Tomography database from 4,154 patients, an AI system is developed that can diagnose NCP and differentiate it from other common pneumonia and normal controls and is made available globally to assist the clinicians to combat COVID-19.
Journal ArticleDOI
Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images.
Ying Song,Shuangjia Zheng,Liang Li,Xiang Zhang,Xiaodong Zhang,Ziwang Huang,Jianwen Chen,Ruixuan Wang,Huiying Zhao,Yunfei Zha,Jun Shen,Yutian Chong,Yuedong Yang +12 more
TL;DR: Wang et al. as mentioned in this paper developed a deep learning-based CT diagnosis system to identify patients with COVID-19, which achieved an AUC of 0.99, recall (sensitivity) of 0.,93, and precision of 0,96.
Posted ContentDOI
Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images
Song Ying,Shuangjia Zheng,Liang Li,Xiang Zhang,Xiaodong Zhang,Ziwang Huang,Jianwen Chen,Huiying Zhao,Ruixuan Wang,Yutian Chong,Jun Shen,Yunfei Zha,Yuedong Yang +12 more
TL;DR: A deep learning-based CT diagnosis system (DeepPneumonia) was developed and showed that the established models can achieve a rapid and accurate identification of COVID-19 in human samples, thereby allowing identification of patients.
Journal ArticleDOI
A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis.
Shuo Wang,Yunfei Zha,Weimin Li,Qingxia Wu,Xiaohu Li,Meng Niu,Meiyun Wang,Xiaoming Qiu,Hongjun Li,He Yu,Wei Gong,Yan Bai,Li Li,Yongbei Zhu,Liusu Wang,Jie Tian +15 more
TL;DR: A fully automatic deep learning system is proposed for COVID-19 diagnostic and prognostic analysis by routinely used computed tomography that automatically focused on abnormal areas that showed consistent characteristics with reported radiological findings.
Journal ArticleDOI
Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: A multicentre study.
Xiangjun Wu,Hui Hui,Meng Niu,Liang Li,Li Wang,Bingxi He,Xin Yang,Li Li,Hongjun Li,Jie Tian,Jie Tian,Yunfei Zha +11 more
TL;DR: Based on deep learning method, the proposed diagnosis model trained on multi-view images of chest CT images showed great potential to improve the efficacy of diagnosis and mitigate the heavy workload of radiologists for the initial screening of COVID-19 pneumonia.