Xiaosong Wang

Computer Vision and Pattern Recognition · 2017–2021

A researcher in the Science corpus: 10 papers; trusts 712 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 256 citations into it; #21,090 of 21,575 by trust here, #6,089 by citations
• Artificial Intelligence — 209 citations into it; #27,010 of 35,345 by trust here, #8,810 by citations
• Radiology, Nuclear Medicine and Imaging — 106 citations into it; #744 of 3,265 by trust here, #188 by citations
• Evaluation & Benchmarks — 49 citations into it; #2,043 of 3,147 by trust here, #725 by citations
• Natural Language Processing — 49 citations into it
• Pulmonary and Respiratory Medicine — 41 citations into it; #180 of 770 by trust here, #63 by citations
• Model Architectures — 39 citations into it
• Multimodal Learning — 39 citations into it; #1,152 of 1,211 by trust here, #846 by citations

Whom Xiaosong Wang trusts overall

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

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

Who trusts Xiaosong Wang overall

1. Pranav Rajpurkar
2. Baoyu Jing
3. Chun-Fu Chen
4. Yuan Li
5. Jeremy Irvin
6. Guanxiong Liu
7. Aurelia Bustos
8. Fenglin Liu
9. Jiancheng Yang
10. Chen Brestel

Papers from Xiaosong Wang's perspective

Ordered by how strongly the researchers Xiaosong 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• ChestX-ray: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases (2019) — 1,263 citations
• TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-Rays (2018) — 560 citations
• Unsupervised Joint Mining of Deep Features and Image Labels for Large-Scale Radiology Image Categorization and Scene Recognition (2017) — 41 citations
• NegBio: a high-performance tool for negation and uncertainty detection in radiology reports (2017) — 229 citations
• DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning (2018) — 562 citations
• Self-supervised Image-text Pre-training With Mixed Data In Chest X-rays (2021) — 29 citations
• When Radiology Report Generation Meets Knowledge Graph (2020) — 410 citations
• Generalizing Deep Learning for Medical Image Segmentation to Unseen Domains via Deep Stacked Transformation (2020) — 514 citations
• Deep learning in medical imaging and radiation therapy. (2018) — 668 citations
• ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases (2017) — 3,535 citations