Hengyong Yu
Radiology, Nuclear Medicine and Imaging · 2008–2017
A researcher in the Science corpus: 5 papers; trusts 45 researchers and is trusted by 11 researchers here, as inferred from the citation graph.
Subfields
- Radiology, Nuclear Medicine and Imaging — 38 citations into it; #711 of 3,265 by trust here, #825 by citations
- Biomedical Engineering — 29 citations into it; #4,672 of 10,734 by trust here, #3,826 by citations
- Computer Vision and Pattern Recognition — 28 citations into it; #11,205 of 21,575 by trust here, #9,563 by citations
- Computational Mechanics — 12 citations into it; #4,820 of 10,001 by trust here, #6,287 by citations
- Media Technology — 10 citations into it
- Artificial Intelligence — 7 citations into it
- Optimization & Training — 6 citations into it
- Radiation — 5 citations into it
Whom Hengyong Yu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Frédéric Noo
- Ge Wang
- Michel Defrise
- Kaiming He
- Rolf Clackdoyle
- Michael Elad
- Andrea Vedaldi
- Julien Mairal
- E.J. Candes
- Idris A. Elbakri
Who trusts Hengyong Yu overall
- Jie Tang
- Zhiqiang Chen
- Xin Yi
- Brian Nett
- Ge Wang
- Xin Jin
- Zhicheng Zhang
- Kyong Hwan Jin
- Jonas Adler
- Guang-Hong Chen
Papers from Hengyong Yu's perspective
Ordered by how strongly the researchers Hengyong Yu trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss (2017) — 1,435 citations
- Low-Dose X-ray CT Reconstruction via Dictionary Learning (2012) — 691 citations
- An outlook on x-ray CT research and development. (2008) — 347 citations
- Convolutional Neural Network based Metal Artifact Reduction in X-ray Computed Tomography (2017) — 446 citations
- Compressed Sensing Based Interior Tomography (2009) — 592 citations