C
Chaoyang Zhang
Researcher at University of Southern Mississippi
Publications - 192
Citations - 3396
Chaoyang Zhang is an academic researcher from University of Southern Mississippi. The author has contributed to research in topics: Medicine & Deep learning. The author has an hindex of 24, co-authored 167 publications receiving 2246 citations. Previous affiliations of Chaoyang Zhang include University of Electronic Science and Technology of China & Xi'an Jiaotong University.
Papers
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Journal ArticleDOI
Digital twin-driven rapid reconfiguration of the automated manufacturing system via an open architecture model
Jiewu Leng,Jiewu Leng,Qiang Liu,Ye Shide,Jing Jianbo,Yan Wang,Chaoyang Zhang,Zhang Ding,Xin Chen +8 more
TL;DR: A novel digital twin-driven approach to achieving improved system performance while minimizing the overheads of the reconfiguration process by automating and rapidly optimizing it is proposed.
Journal ArticleDOI
Deep Learning Based Analysis of Histopathological Images of Breast Cancer.
TL;DR: The experimental results demonstrate that using the proposed autoencoder network results in better clustering results than those based on features extracted only by Inception_ResNet_V2 network, which is the best deep learning architecture so far for diagnosing breast cancers by analyzing histopathological images.
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Fluorescence-enhanced optical imaging in large tissue volumes using a gain-modulated ICCD camera.
Anuradha Godavarty,Margaret J. Eppstein,Chaoyang Zhang,Sangeeta Theru,Alan B. Thompson,Michael Gurfinkel,Eva M. Sevick-Muraca +6 more
TL;DR: This work represents the first time that 3D fluorescence-enhanced optical tomographic reconstructions have been achieved from experimental measurements of the time-dependent light propagation on a clinically relevant breast-shaped tissue phantom using a gain-modulated ICCD camera.
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Diagnostic imaging of breast cancer using fluorescence-enhanced optical tomography: phantom studies.
Anuradha Godavarty,Alan B. Thompson,Ranadhir Roy,Mikhail Gurfinkel,Margaret J. Eppstein,Chaoyang Zhang,Eva M. Sevick-Muraca +6 more
TL;DR: These studies represent the first 3-D tomographic images from physiologically relevant geometries for breast imaging from 2-D boundary surface measurements using the modified truncated Newton's method.
Journal ArticleDOI
Deep learning architectures for multi-label classification of intelligent health risk prediction.
Andrew S. Maxwell,Runzhi Li,Bei Yang,Heng Weng,Aihua Ou,Huixiao Hong,Zhaoxian Zhou,Ping Gong,Chaoyang Zhang +8 more
TL;DR: Preliminary results suggest that Deep Neural Networks (DNN), a DL architecture, when applied to multi-label classification of chronic diseases, produced accuracy that was comparable to that of common methods such as Support Vector Machines.