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Hongming Shan
Researcher at Fudan University
Publications - 133
Citations - 3115
Hongming Shan is an academic researcher from Fudan University. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 17, co-authored 99 publications receiving 1598 citations. Previous affiliations of Hongming Shan include Rensselaer Polytechnic Institute & Northeastern University.
Papers
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Journal ArticleDOI
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.
Journal ArticleDOI
Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction.
Hongming Shan,Atul Padole,Fatemeh Homayounieh,Uwe Kruger,Ruhani Doda Khera,Chayanin Nitiwarangkul,Chayanin Nitiwarangkul,Mannudeep K. Kalra,Ge Wang +8 more
TL;DR: In this article, a modularized neural network for low-dose CT (LDCT) was proposed and compared with commercial iterative reconstruction methods from three leading CT vendors, and the learned workflow allows radiologists-in-the-loop to optimize the denoising depth in a task-specific fashion.
Journal ArticleDOI
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.
Journal ArticleDOI
CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)
Chenyu You,Guang Li,Yi Zhang,Xiaoliu Zhang,Hongming Shan,Shenghong Ju,Zhen Zhao,Zhuiyang Zhang,Wenxiang Cong,Michael W. Vannier,Punam K. Saha,Ge Wang +11 more
TL;DR: In this article, a semi-supervised deep learning approach was proposed to recover high-resolution (HR) CT images from low resolution (LR) counterparts by enforcing the cycle-consistency in terms of Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs.
Journal ArticleDOI
Structurally-Sensitive Multi-Scale Deep Neural Network for Low-Dose CT Denoising
Chenyu You,Qingsong Yang,Hongming Shan,Lars Gjesteby,Guang Li,Shenghong Ju,Zhuiyang Zhang,Zhen Zhao,Yi Zhang,Wenxiang Cong,Ge Wang +10 more
TL;DR: This paper proposes a novel 3-D noise reduction method, called structurally sensitive multi-scale generative adversarial net, to improve the low-dose CT image quality, which incorporates3-D volumetric information to improved the image quality.