Paul S. Babyn

Computer Vision and Pattern Recognition · 2006–2018

A researcher in the Science corpus: 5 papers; trusts 536 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 137 citations into it; #10,763 of 21,575 by trust here, #8,952 by citations
• Generative Models — 71 citations into it; #1,302 of 1,956 by trust here, #1,091 by citations
• Artificial Intelligence — 56 citations into it; #22,407 of 35,345 by trust here, #12,580 by citations
• Radiology, Nuclear Medicine and Imaging — 52 citations into it; #1,850 of 3,265 by trust here, #2,195 by citations
• Media Technology — 22 citations into it
• Biomedical Engineering — 16 citations into it; #6,153 of 10,734 by trust here, #8,044 by citations
• Model Architectures — 16 citations into it
• Biophysics — 15 citations into it

Whom Paul S. Babyn trusts overall

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

1. Geoffrey E. Hinton
2. Karen Simonyan
3. Alex Krizhevsky
4. Kaiming He
5. Diederik P. Kingma
6. Yoshua Bengio
7. Yann LeCun
8. Individualized Treat
9. Andrew Zisserman
10. Ilya Sutskever

Who trusts Paul S. Babyn overall

1. Martin J. Willemink
2. Sergey I. Nikolenko
3. Peter B. Noël

Papers from Paul S. Babyn's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Attention is All you Need (Neural Information Processing Systems)
8. Long Short-Term Memory
9. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Generative Adversarial Network in Medical Imaging: A Review (2018) — 1,691 citations
• Sharpness-Aware Low-Dose CT Denoising Using Conditional Generative Adversarial Network (2017) — 319 citations
• Thyroid Nodule Classification in Ultrasound Images by Fine-Tuning Deep Convolutional Neural Network (2017) — 368 citations
• Principles, techniques, and applications of T2*-based MR imaging and its special applications. (2009) — 712 citations
• US or CT for Diagnosis of Appendicitis in Children and Adults? A Meta-Analysis. (2006) — 785 citations