Qian Wang

Computer Vision and Pattern Recognition · IEEE Transactions on Medical Imaging · 2015–2022

A researcher in the Science corpus: 8 papers; trusts 271 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 227 citations into it; #16,669 of 21,575 by trust here, #8,722 by citations
• Artificial Intelligence — 129 citations into it
• Radiology, Nuclear Medicine and Imaging — 109 citations into it; #2,929 of 3,265 by trust here, #1,444 by citations
• Model Architectures — 37 citations into it
• Biophysics — 29 citations into it
• Media Technology — 23 citations into it
• Biomedical Engineering — 20 citations into it
• Representation & Self-Supervised Learning — 19 citations into it

Whom Qian Wang trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Krizhevsky
4. Honglak Lee
5. Yann LeCun
6. Kaiming He
7. Diederik P. Kingma
8. Ilya Sutskever
9. Andrew Y. Ng
10. Hugo Larochelle

Who trusts Qian Wang overall

1. Arvid Lundervold

Papers from Qian Wang's perspective

Ordered by how strongly the researchers Qian Wang 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. Attention is All you Need (Neural Information Processing Systems)
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Long Short-Term Memory
8. ImageNet: A large-scale hierarchical image database
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A Multi-Organ Nucleus Segmentation Challenge (2020) — 533 citations
• Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19 (2020) — 1,123 citations
• Deformable Image Registration Using Cue-aware Deep Regression Network (2018) — 118 citations
• Transformers in Medical Image Analysis: A Review (2022) — 401 citations
• Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia (2020) — 328 citations
• Medical Image Synthesis with Deep Convolutional Adversarial Networks (2018) — 562 citations
• Deep Auto-context Convolutional Neural Networks for Standard-Dose PET Image Estimation from Low-Dose PET/MRI (2017) — 289 citations
• Scalable High Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning (2015) — 274 citations