Guotai Wang

Computer Vision and Pattern Recognition · 2017–2022

A researcher in the Science corpus: 13 papers; trusts 379 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 270 citations into it; #15,661 of 21,575 by trust here, #4,001 by citations
• Artificial Intelligence — 175 citations into it
• Radiology, Nuclear Medicine and Imaging — 61 citations into it; #2,955 of 3,265 by trust here, #1,299 by citations
• Model Architectures — 57 citations into it
• Biomedical Engineering — 35 citations into it
• Representation & Self-Supervised Learning — 29 citations into it
• Optimization & Training — 27 citations into it
• Biophysics — 19 citations into it

Whom Guotai Wang 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. Ross B. Girshick
6. Yoshua Bengio
7. Karen Simonyan
8. X. Zhang
9. Alex Graves
10. Yann LeCun

Who trusts Guotai Wang overall

1. Samuel Budd
2. Xiangde Luo
3. Rushi Jiao
4. Tao Lei
5. Akmalbek Bobomirzaevich Abdusalomov
6. Soumajit Majumder
7. Wenqi Li
8. Emma Claire Robinson
9. Gustav Bredell
10. Shervin Minaee

Papers from Guotai Wang's perspective

Ordered by how strongly the researchers Guotai 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. 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. Going deeper with convolutions

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
• 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
• Semi-supervised Medical Image Segmentation through Dual-task Consistency (2020) — 796 citations
• Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency (2022) — 383 citations
• Semi-Supervised Medical Image Segmentation via Cross Teaching between CNN and Transformer (2021) — 332 citations
• Efficient Semi-supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency (2020) — 325 citations
• A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions From CT Images (2020) — 418 citations
• CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation (2020) — 655 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