Z
Zhenyu Tang
Researcher at Beihang University
Publications - 33
Citations - 1801
Zhenyu Tang is an academic researcher from Beihang University. The author has contributed to research in topics: Image registration & Deep learning. The author has an hindex of 11, co-authored 28 publications receiving 1103 citations. Previous affiliations of Zhenyu Tang include University of Duisburg-Essen & Anhui University.
Papers
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Journal ArticleDOI
Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19
Feng Shi,Jun Wang,Jun Shi,Ziyan Wu,Qian Wang,Zhenyu Tang,Kelei He,Yinghuan Shi,Dinggang Shen +8 more
TL;DR: This review paper covers the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up, and particularly focuses on the integration of AI with X-ray and CT, both of which are widely used in the frontline hospitals.
Journal ArticleDOI
Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19
Feng Shi,Jun Wang,Jun Shi,Ziyan Wu,Qian Wang,Zhenyu Tang,Kelei He,Yinghuan Shi,Dinggang Shen +8 more
TL;DR: In this article, the authors reviewed the rapid responses in the community of medical imaging (empowered by AI) toward COVID-19, including image acquisition, segmentation, diagnosis, and follow-up.
Posted Content
Severity Assessment of Coronavirus Disease 2019 (COVID-19) Using Quantitative Features from Chest CT Images
TL;DR: The RF based model can achieve automatic severity assessment (non-severe or severe) of COVID-19 infection, and the performance is promising.
Journal ArticleDOI
Deep Spatial-Temporal Feature Fusion From Adaptive Dynamic Functional Connectivity for MCI Identification
TL;DR: A novel adaptive dFC model is proposed, aided by a deep spatial-temporal feature fusion method, for mild cognitive impairment (MCI) identification, which effectively alleviates the problem of parameter optimization and elucidate the superiority of the proposed method for MCI classification.
Journal ArticleDOI
Severity assessment of COVID-19 using CT image features and laboratory indices.
TL;DR: Several chest CT image features and laboratory indices are found to be highly related to COVID-19 severity, which could be valuable for the clinical diagnosis of CO VID-19.