Y
Yin Zhang
Researcher at University of Electronic Science and Technology of China
Publications - 322
Citations - 7094
Yin Zhang is an academic researcher from University of Electronic Science and Technology of China. The author has contributed to research in topics: Radar & Radar imaging. The author has an hindex of 35, co-authored 273 publications receiving 4960 citations. Previous affiliations of Yin Zhang include Huazhong University of Science and Technology & Nanjing University.
Papers
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Health-CPS: Healthcare Cyber-Physical System Assisted by Cloud and Big Data
TL;DR: The results of this study show that the technologies of cloud and big data can be used to enhance the performance of the healthcare system so that humans can then enjoy various smart healthcare applications and services.
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Deep Feature Learning for Medical Image Analysis with Convolutional Autoencoder Neural Network
TL;DR: A convolutional autoencoder deep learning framework to support unsupervised image features learning for lung nodule through unlabeled data, which only needs a small amount of labeled data for efficient feature learning.
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Deep convolutional neural networks for diabetic retinopathy detection by image classification
Shaohua Wan,Yan Liang,Yin Zhang +2 more
TL;DR: This paper brings convolutional neural networks power to DR detection, which includes 3 major difficult challenges: classification, segmentation and detection, and adopts AlexNet, VggNet, GoogleNet, ResNet, and analyze how well these models do with the DR image classification.
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Wearable 2.0: Enabling Human-Cloud Integration in Next Generation Healthcare Systems
TL;DR: A Wearable 2.0 healthcare system to improve QoE and QoS of the next generation healthcare system is proposed and washable smart clothing is the critical component to collect users' physiological data and receive the analysis results of users’ health and emotional status provided by cloud-based machine intelligence.
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iDoctor: Personalized and professionalized medical recommendations based on hybrid matrix factorization
TL;DR: The experimental results show that iDoctor provides a higher predication rating and increases the accuracy of healthcare recommendation significantly, and is compared with previous healthcare recommendation methods using real datasets.