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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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Digital twin-driven rapid reconfiguration of the automated manufacturing system via an open architecture model

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.
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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.

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.

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.
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Deep learning architectures for multi-label classification of intelligent health risk prediction.

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.