Wenjia Bai

Computer Vision and Pattern Recognition · 2013–2020

A researcher in the Science corpus: 12 papers; trusts 224 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 164 citations into it; #21,321 of 21,575 by trust here, #5,236 by citations
• Artificial Intelligence — 88 citations into it
• Radiology, Nuclear Medicine and Imaging — 68 citations into it
• Model Architectures — 32 citations into it
• Biomedical Engineering — 28 citations into it
• Media Technology — 20 citations into it
• Biophysics — 13 citations into it
• Computational Mechanics — 9 citations into it

Whom Wenjia Bai trusts overall

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

1. Karen Simonyan
2. Kaiming He
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Alex Krizhevsky
6. Andrew Zisserman
7. Yoshua Bengio
8. Yann LeCun
9. Evan Shelhamer
10. Christian Szegedy

Who trusts Wenjia Bai overall

1. Xiangde Luo
2. Ozan Oktay
3. Chen Chen
4. Andriy Myronenko
5. Agisilaos Chartsias
6. James R. Clough
7. S. Kevin Zhou
8. Xiaomeng Li
9. Yingda Xia
10. Lequan Yu

Papers from Wenjia Bai's perspective

Ordered by how strongly the researchers Wenjia Bai 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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks (2016) — 410 citations
• Semi-supervised Learning for Network-Based Cardiac MR Image Segmentation (2017) — 431 citations
• Cardiac Image Super-Resolution with Global Correspondence Using Multi-Atlas PatchMatch (2013) — 264 citations
• Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation (2017) — 732 citations
• A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion (2015) — 170 citations
• A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging (2020) — 478 citations
• Deep Learning for Cardiac Image Segmentation: A Review (2019) — 844 citations
• Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents (2018) — 61 citations
• Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling (2018) — 77 citations
• Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation (2017) — 460 citations
• Statistical shape modeling of the left ventricle: myocardial infarct classification challenge (2017) — 91 citations
• Automated cardiovascular magnetic resonance image analysis with fully convolutional networks (2017) — 706 citations