Lei Xing

Computer Vision and Pattern Recognition · Medical Physics (Lancaster) · 2003–2021

A researcher in the Science corpus: 9 papers; trusts 142 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 81 citations into it
• Radiology, Nuclear Medicine and Imaging — 50 citations into it; #1,674 of 3,265 by trust here, #814 by citations
• Artificial Intelligence — 43 citations into it
• Computational Mechanics — 30 citations into it
• Generative Models — 28 citations into it
• Biomedical Engineering — 20 citations into it; #10,327 of 10,734 by trust here, #8,044 by citations
• Optimization & Training — 15 citations into it
• Model Architectures — 13 citations into it

Whom Lei Xing trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Sergey Ioffe
5. Yang Song
6. Kaiming He
7. Ilya Sutskever
8. Yoshua Bengio
9. Christian Szegedy
10. Pascal Vincent

Who trusts Lei Xing overall

1. Hyungjin Chung
2. Hyunseok Seo
3. Lin Shi
4. Arvid Lundervold
5. Berkman Sahiner
6. Jong Chul Ye
7. Liyue Shen
8. Yang Song

Papers from Lei Xing's perspective

Ordered by how strongly the researchers Lei Xing trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Deep Generative Adversarial Networks for Compressed Sensing Automates MRI (2017) — 164 citations
• Modified U-Net (mU-Net) with Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images (2019) — 324 citations
• Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning (2019) — 302 citations
• Solving Inverse Problems in Medical Imaging with Score-Based Generative Models (2021) — 774 citations
• NeRP: Implicit Neural Representation Learning with Prior Embedding for Sparsely Sampled Image Reconstruction (2021) — 316 citations
• Deep Generative Adversarial Neural Networks for Compressive Sensing (GANCS) MRI (2018) — 552 citations
• Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks (2017) — 472 citations
• GPU computing in medical physics: a review. (2011) — 314 citations
• Guidance document on delivery, treatment planning, and clinical implementation of IMRT: report of the IMRT Subcommittee of the AAPM Radiation Therapy Committee. (2003) — 910 citations