T
Tianfu Wang
Researcher at Shenzhen University
Publications - 357
Citations - 9549
Tianfu Wang is an academic researcher from Shenzhen University. The author has contributed to research in topics: Segmentation & Computer science. The author has an hindex of 39, co-authored 321 publications receiving 5959 citations. Previous affiliations of Tianfu Wang include University of Hong Kong & Emory University.
Papers
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Journal ArticleDOI
A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images
TL;DR: A wide and deep neural network with strong induction ability is proposed to model the transformation, and an efficient training strategy is presented, where instead of a single label of the center pixel, the network can output the label map of all pixels for a given image patch.
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Glucose-Responsive Sequential Generation of Hydrogen Peroxide and Nitric Oxide for Synergistic Cancer Starving-Like/Gas Therapy.
Wenpei Fan,Wenpei Fan,Nan Lu,Peng Huang,Yi Liu,Zhen Yang,Sheng Wang,Guocan Yu,Yijing Liu,Junkai Hu,Qianjun He,Junle Qu,Tianfu Wang,Xiaoyuan Chen +13 more
TL;DR: A novel treatment paradigm of starving-like therapy is developed for significant tumor-killing effects, more effective than conventional starving therapy by only cutting off the energy supply by using hollow mesoporous organosilica nanoparticle as a biocompatible/biodegradable nanocarrier.
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Deep Learning in Medical Ultrasound Analysis: A Review
TL;DR: Several popular deep learning architectures are briefly introduced, and their applications in various specific tasks in US image analysis, such as classification, detection, and segmentation are discussed.
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Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks
TL;DR: This paper presents a learning-based approach to locate the fetal abdominal standard plane (FASP) in US videos by constructing a domain transferred deep convolutional neural network (CNN) that outperforms the state-of-the-art method for the FASP localization.
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Accurate Segmentation of Cervical Cytoplasm and Nuclei Based on Multiscale Convolutional Network and Graph Partitioning
TL;DR: A multiscale convolutional network (MSCN) and graph-partitioning-based method is proposed for accurate segmentation of cervical cytoplasm and nuclei and a coarse-to-fine nucleus segmentation framework is developed.