Y
Yi Wang
Researcher at Shenzhen University
Publications - 55
Citations - 1343
Yi 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 11, co-authored 44 publications receiving 684 citations. Previous affiliations of Yi Wang include The Chinese University of Hong Kong.
Papers
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Journal ArticleDOI
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.
Journal ArticleDOI
Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound
Yi Wang,Dong Ni,Haoran Dou,Xiaowei Hu,Lei Zhu,Xin Yang,Ming Xu,Jing Qin,Pheng-Ann Heng,Tianfu Wang +9 more
TL;DR: Wu et al. as discussed by the authors developed a 3D deep neural network equipped with attention modules for better prostate segmentation in transrectal ultrasound (TRUS) images by fully exploiting the complementary information encoded in different layers of the convolutional neural network.
Journal ArticleDOI
Deeply-Supervised Networks With Threshold Loss for Cancer Detection in Automated Breast Ultrasound
Yi Wang,Na Wang,Min Xu,Junxiong Yu,Chenchen Qin,Xiao Luo,Xin Yang,Tianfu Wang,Anhua Li,Dong Ni +9 more
TL;DR: An innovative 3D convolutional network is offered which is used for ABUS for automated cancer detection, in order to accelerate reviewing and meanwhile to obtain high detection sensitivity with low false positives (FPs).
Book ChapterDOI
Deep Attentional Features for Prostate Segmentation in Ultrasound
TL;DR: A novel deep neural network equipped with deep attentional feature (DAF) modules for better prostate segmentation in TRUS by fully exploiting the complementary information encoded in different layers of the convolutional neural network (CNN).
Journal ArticleDOI
Automatic Fetal Head Circumference Measurement in Ultrasound Using Random Forest and Fast Ellipse Fitting
TL;DR: A learning-based framework that used prior knowledge and employed a fast ellipse fitting method (ElliFit) to measure HC automatically and demonstrated that the method shows great promise for applications in clinical practice.