J
Jun Shi
Researcher at Shanghai University
Publications - 147
Citations - 4641
Jun Shi is an academic researcher from Shanghai University. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 27, co-authored 132 publications receiving 3202 citations. Previous affiliations of Jun Shi include Hong Kong Polytechnic University & Delft University of Technology.
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.
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Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease
TL;DR: Experimental results indicate that MM-SDPN is superior over the state-of-the-art multimodal feature-learning-based algorithms for AD diagnosis.
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.
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Deep learning based classification of breast tumors with shear-wave elastography.
TL;DR: A deep learning architecture for automated extraction of learned-from-data image features from the shear-wave elastography (SWE) that integrates feature learning with feature selection on SWE is built and may be potentially used in clinical computer-aided diagnosis of breast cancer.
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Sonomyography: Monitoring morphological changes of forearm muscles in actions with the feasibility for the control of powered prosthesis
TL;DR: It was demonstrated that the morphological changes of forearm muscles during actions can be successfully detected by ultrasound and linearly correlated with the wrist angle, and sonomyography had potentials for the musculoskeletal control and assessment.