Daguang Xu

Computer Vision and Pattern Recognition · 2017–2022

A researcher in the Science corpus: 17 papers; trusts 614 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 313 citations into it; #10,299 of 21,575 by trust here, #4,834 by citations
• Artificial Intelligence — 267 citations into it
• Radiology, Nuclear Medicine and Imaging — 132 citations into it; #1,689 of 3,265 by trust here, #368 by citations
• Model Architectures — 72 citations into it
• Biomedical Engineering — 47 citations into it
• Optimization & Training — 42 citations into it
• Representation & Self-Supervised Learning — 42 citations into it
• Neurology — 31 citations into it

Whom Daguang Xu trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Ross B. Girshick
6. Yoshua Bengio
7. Yann LeCun
8. Ilya Sutskever
9. Shaoqing Ren
10. Jimmy Ba

Who trusts Daguang Xu overall

1. Fahad Shahbaz Khan
2. Salman Hameed Khan
3. Bo Wang
4. Jun Ma
5. Feifei Li
6. Vishal M. Patel
7. Jeya Maria Jose Valanarasu
8. Yin Dai
9. Yifan Gao

Papers from Daguang Xu's perspective

Ordered by how strongly the researchers Daguang Xu 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
• The Medical Segmentation Decathlon (2021) — 1,513 citations
• Combo Loss: Handling Input and Output Imbalance in Multi-Organ Segmentation (2018) — 434 citations
• Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images (2022) — 2,376 citations
• The future of digital health with federated learning (2020) — 2,960 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
• Federated learning for predicting clinical outcomes in patients with COVID-19 (2021) — 736 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
• VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images (2020) — 372 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
• Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets (2020) — 535 citations
• Privacy-preserving Federated Brain Tumour Segmentation (2019) — 562 citations
• Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation (2019) — 50 citations
• 3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes (2017) — 158 citations