Dong Yang

Computer Vision and Pattern Recognition · IEEE Workshop/Winter Conference on Applications of Computer Vision · 2010–2022

A researcher in the Science corpus: 10 papers; trusts 476 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 217 citations into it; #10,338 of 21,575 by trust here, #6,206 by citations
• Artificial Intelligence — 153 citations into it
• Radiology, Nuclear Medicine and Imaging — 81 citations into it; #1,690 of 3,265 by trust here, #684 by citations
• Model Architectures — 50 citations into it
• Representation & Self-Supervised Learning — 40 citations into it
• Biomedical Engineering — 24 citations into it
• Neurology — 19 citations into it
• Natural Language Processing — 15 citations into it

Whom Dong Yang trusts overall

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

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

Who trusts Dong Yang overall

1. Fahad Shamshad
2. Jun Ma
3. Fabian Isensee
4. Junde Wu
5. Xiaohong Huang
6. Jeya Maria Jose Valanarasu
7. Yin Dai
8. Haotian Tang
9. Hong-Yu Zhou
10. Zhaohu Xing

Papers from Dong Yang's perspective

Ordered by how strongly the researchers Dong Yang 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• UNETR: Transformers for 3D Medical Image Segmentation (2021) — 2,828 citations
• Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images (2022) — 2,376 citations
• MONAI: An open-source framework for deep learning in healthcare (2022) — 996 citations
• Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis (2021) — 876 citations
• Self-supervised Image-text Pre-training With Mixed Data In Chest X-rays (2021) — 29 citations
• Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation (2020) — 273 citations
• Generalizing Deep Learning for Medical Image Segmentation to Unseen Domains via Deep Stacked Transformation (2020) — 514 citations
• Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets (2020) — 535 citations
• Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation (2019) — 50 citations
• Jointly Optimizing a Two-Step Conditional Random Field Model for Machine Transliteration and Its Fast Decoding Algorithm (2010) — 8 citations