Wenqi Li

Computer Vision and Pattern Recognition · 2017–2022

A researcher in the Science corpus: 13 papers; trusts 337 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 218 citations into it; #12,668 of 21,575 by trust here, #3,323 by citations
• Artificial Intelligence — 165 citations into it
• Model Architectures — 49 citations into it
• Biomedical Engineering — 34 citations into it
• Optimization & Training — 27 citations into it
• Neurology — 20 citations into it
• Biophysics — 16 citations into it
• Media Technology — 15 citations into it

Whom Wenqi Li trusts overall

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

1. Kaiming He
2. Diederik P. Kingma
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Ross B. Girshick
7. Jimmy Ba
8. Yann LeCun
9. X. Zhang
10. Christian Szegedy

Who trusts Wenqi Li overall

1. Guotai Wang
2. Md. Zahangir Alom
3. Fabian Isensee
4. Fan Lai
5. Sébastien Ourselin
6. Felix J. S. Bragman
7. Ali Hatamizadeh
8. Samuel Budd
9. Xiangde Luo
10. Mingxia Liu

Papers from Wenqi Li's perspective

Ordered by how strongly the researchers Wenqi Li 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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

• DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation (2017) — 476 citations
• Automatic Brain Tumor Segmentation Using Cascaded Anisotropic Convolutional Neural Networks (2017) — 546 citations
• Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations (2017) — 2,736 citations
• The future of digital health with federated learning (2020) — 2,960 citations
• NiftyNet: a deep-learning platform for medical imaging (2017) — 565 citations
• Weakly-supervised convolutional neural networks for multimodal image registration (2018) — 442 citations
• Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks (2018) — 730 citations
• 2017 Robotic Instrument Segmentation Challenge (2019) — 310 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
• Privacy-preserving Federated Brain Tumour Segmentation (2019) — 562 citations
• Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning (2017) — 764 citations
• On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task (2017) — 364 citations