Lequan Yu

Computer Vision and Pattern Recognition · IEEE Transactions on Medical Imaging · 2016–2021

A researcher in the Science corpus: 22 papers; trusts 1,052 researchers and is trusted by 33 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 452 citations into it; #6,182 of 21,575 by trust here, #2,688 by citations
• Artificial Intelligence — 298 citations into it; #8,286 of 35,345 by trust here, #4,489 by citations
• Radiology, Nuclear Medicine and Imaging — 125 citations into it; #565 of 3,265 by trust here, #350 by citations
• Model Architectures — 108 citations into it
• Computational Mechanics — 63 citations into it; #4,479 of 10,001 by trust here, #4,315 by citations
• Representation & Self-Supervised Learning — 62 citations into it
• Biomedical Engineering — 47 citations into it; #3,505 of 10,734 by trust here, #2,677 by citations
• Optimization & Training — 47 citations into it

Whom Lequan Yu trusts overall

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

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

Who trusts Lequan Yu overall

1. Xiangde Luo
2. Hao Chen
3. Qi-Yan Dou
4. Md. Zahangir Alom
5. Yifan Wang
6. Ruihui Li
7. Balázs Harangi
8. P. Heng
9. Chi-Wing Fu
10. Quande Liu

Papers from Lequan Yu's perspective

Ordered by how strongly the researchers Lequan Yu 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. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
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

• VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation (2016) — 144 citations
• Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization (2020) — 231 citations
• Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision Challenge (2017) — 414 citations
• Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation (2019) — 495 citations
• MS-Net: Multi-Site Network for Improving Prostate Segmentation With Heterogeneous MRI Data (2020) — 356 citations
• 3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes (2016) — 370 citations
• Volumetric ConvNets with Mixed Residual Connections for Automated Prostate Segmentation from 3D MR Images (2017) — 358 citations
• nnFormer: Volumetric Medical Image Segmentation via a 3D Transformer (2021) — 731 citations
• DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation (2016) — 636 citations
• 3D deeply supervised network for automated segmentation of volumetric medical images (2017) — 567 citations
• Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation (2020) — 273 citations
• Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation (2019) — 1,259 citations
• Difficulty-aware Meta-Learning for Rare Disease Diagnosis (2019) — 75 citations
• CANet: Cross-Disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading (2019) — 367 citations
• EC-Net: an Edge-aware Point set Consolidation Network (2018) — 296 citations
• SV-RCNet: Workflow Recognition From Surgical Videos Using Recurrent Convolutional Network (2018) — 318 citations
• PU-Net: Point Cloud Upsampling Network (2018) — 730 citations
• Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection (2017) — 506 citations
• DCAN: Deep contour‐aware networks for object instance segmentation from histology images (2017) — 492 citations
• VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images (2017) — 709 citations