C
Congzhi Wang
Researcher at Chinese Academy of Sciences
Publications - 97
Citations - 1186
Congzhi Wang is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Ultrasonic sensor & Imaging phantom. The author has an hindex of 12, co-authored 84 publications receiving 754 citations. Previous affiliations of Congzhi Wang include Hong Kong Polytechnic University.
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Journal ArticleDOI
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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Bioinspired in-sensor visual adaptation for accurate perception
Fuyou Liao,Zheng Zhou,Beom Jin Kim,Jiewei Chen,Jingli Wang,Tianqing Wan,Yue Zhou,Anh Tuan Hoang,Congzhi Wang,Jinfeng Kang,J.H. Ahn,Yang Chai +11 more
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Improved Anatomical Specificity of Non-invasive Neuro-stimulation by High Frequency (5 MHz) Ultrasound
Guofeng Li,Huixia Zhao,Hui Zhou,Fei Yan,Wang Jingyao,Chang-Xi Xu,Congzhi Wang,Lili Niu,Long Meng,Song Wu,Huailing Zhang,Weibao Qiu,Hairong Zheng +12 more
TL;DR: High frequency (5 MHz) ultrasound can successfully activate the brain circuits in mice and provides a smaller stimulation region, which offers improved anatomical specificity for neuro-stimulation in a non-invasive manner.
Journal ArticleDOI
Influence of measurement depth on the stiffness assessment of healthy liver with real-time shear wave elastography.
Congzhi Wang,Jian Zheng,Zeping Huang,Yang Xiao,Dan Song,Jie Zeng,Hairong Zheng,Rongqin Zheng +7 more
TL;DR: According to the results, the depth range for the most reliable liver stiffness assessment using SWE should be 3-5 cm from the probe surface and simultaneously 1-2 cm below the liver capsule.
Journal ArticleDOI
Quantification of elastic heterogeneity using contourlet-based texture analysis in shear-wave elastography for breast tumor classification.
TL;DR: The results demonstrated that the contourlet-based texture features captured the tumor's elastic heterogeneity and improved diagnostic performance contrasted with the classic features.