Xin Yang

Computer Vision and Pattern Recognition · Medical Image Anal. · 2015–2020

A researcher in the Science corpus: 8 papers; trusts 190 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 156 citations into it
• Artificial Intelligence — 100 citations into it
• Radiology, Nuclear Medicine and Imaging — 55 citations into it
• Model Architectures — 43 citations into it
• Biomedical Engineering — 27 citations into it
• Optimization & Training — 17 citations into it
• Media Technology — 16 citations into it
• Representation & Self-Supervised Learning — 16 citations into it

Whom Xin Yang trusts overall

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

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

Who trusts Xin Yang overall

1. Quande Liu
2. Ali Hatamizadeh

Papers from Xin Yang's perspective

Ordered by how strongly the researchers Xin 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet Large Scale Visual Recognition Challenge
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• Volumetric ConvNets with Mixed Residual Connections for Automated Prostate Segmentation from 3D MR Images (2017) — 358 citations
• 3D deeply supervised network for automated segmentation of volumetric medical images (2017) — 567 citations
• Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge (2019) — 346 citations
• Uncertainty-aware domain alignment for anatomical structure segmentation (2020) — 60 citations
• A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging (2020) — 478 citations
• Deep Learning in Medical Ultrasound Analysis: A Review (2019) — 723 citations
• Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved? (2018) — 2,285 citations
• Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks (2015) — 348 citations