Y
Yi Zhang
Researcher at Sichuan University
Publications - 73
Citations - 3928
Yi Zhang is an academic researcher from Sichuan University. The author has contributed to research in topics: Iterative reconstruction & Deep learning. The author has an hindex of 19, co-authored 69 publications receiving 2507 citations. Previous affiliations of Yi Zhang include Southern Medical University.
Papers
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Journal ArticleDOI
Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss
Qingsong Yang,Pingkun Yan,Yanbo Zhang,Hengyong Yu,Yongyi Shi,Xuanqin Mou,Mannudeep K. Kalra,Yi Zhang,Ling Sun,Ge Wang +9 more
TL;DR: Wang et al. as mentioned in this paper introduced a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity, which is capable of not only reducing the image noise level but also trying to keep the critical information at the same time.
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Low-dose CT via convolutional neural network
TL;DR: A deep convolutional neural network is here used to map low-dose CT images towards its corresponding normal-dose counterparts in a patch-by-patch fashion, demonstrating a great potential of the proposed method on artifact reduction and structure preservation.
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3-D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning From a 2-D Trained Network
Hongming Shan,Yi Zhang,Qingsong Yang,Uwe Kruger,Mannudeep K. Kalra,Ling Sun,Wenxiang Cong,Ge Wang +7 more
TL;DR: A conveying path-based convolutional encoder-decoder (CPCE) network in 2-D and 3-D configurations within the GAN framework for LDCT denoising, which has a better performance in that it suppresses image noise and preserves subtle structures.
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Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)
TL;DR: Zhang et al. as discussed by the authors combined the autoencoder, the deconvolution network, and shortcut connections into the residual encoder-decoder convolutional neural network (RED-CNN) for low-dose CT imaging.
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CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE)
Chenyu You,Wenxiang Cong,Michael W. Vannier,Punam K. Saha,Eric A. Hoffman,Ge Wang,Guang Li,Yi Zhang,Xiaoliu Zhang,Hongming Shan,Mengzhou Li,Shenghong Ju,Zhen Zhao,Zhuiyang Zhang +13 more
TL;DR: Wang et al. as mentioned in this paper proposed a semi-supervised deep learning approach to recover high-resolution (HR) CT images from low resolution (LR) counterparts by enforcing the cycle-consistency in terms of the Wasserstein distance.