Qi-Yan Dou

Computer Vision and Pattern Recognition · 2016–2022

A researcher in the Science corpus: 26 papers; trusts 1,186 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 507 citations into it; #6,053 of 21,575 by trust here, #2,316 by citations
• Artificial Intelligence — 474 citations into it; #7,366 of 35,345 by trust here, #2,050 by citations
• Radiology, Nuclear Medicine and Imaging — 170 citations into it; #422 of 3,265 by trust here, #208 by citations
• Model Architectures — 113 citations into it
• Representation & Self-Supervised Learning — 92 citations into it
• Optimization & Training — 72 citations into it
• Biomedical Engineering — 57 citations into it; #10,403 of 10,734 by trust here, #2,515 by citations
• Pulmonary and Respiratory Medicine — 50 citations into it

Whom Qi-Yan Dou trusts overall

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

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

Who trusts Qi-Yan Dou overall

1. Hao Chen
2. Lequan Yu
3. Cheng Chen
4. Quande Liu
5. Holger R. Roth
6. P. Heng
7. Md. Zahangir Alom
8. Khushboo Munir
9. Jing Qin
10. Satya Prakash Singh

Papers from Qi-Yan Dou's perspective

Ordered by how strongly the researchers Qi-Yan Dou 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
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. 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

• Domain Generalization via Model-Agnostic Learning of Semantic Features (2019) — 812 citations
• The Liver Tumor Segmentation Benchmark (LiTS) (2019) — 1,500 citations
• VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation (2016) — 144 citations
• Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer (2017) — 3,174 citations
• H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes (2018) — 2,217 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
• Harmonizing Transferability and Discriminability for Adapting Object Detectors (2020) — 335 citations
• FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space (2021) — 617 citations
• 3D deeply supervised network for automated segmentation of volumetric medical images (2017) — 567 citations
• Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation (2019) — 374 citations
• 3D multi‐scale FCN with random modality voxel dropout learning for Intervertebral Disc Localization and Segmentation from Multi‐modality MR Images (2018) — 138 citations
• Federated learning enables big data for rare cancer boundary detection (2022) — 346 citations
• FedBN: Federated Learning on Non-IID Features via Local Batch Normalization (2021) — 1,240 citations
• Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation (2020) — 398 citations
• Weakly Supervised Deep Learning for Whole Slide Lung Cancer Image Analysis (2020) — 373 citations
• Unpaired Multi-Modal Segmentation via Knowledge Distillation (2020) — 216 citations
• Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations with Adversarial Loss (2018) — 329 citations
• MILD‐Net: Minimal information loss dilated network for gland instance segmentation in colon histology images (2018) — 366 citations
• SV-RCNet: Workflow Recognition From Surgical Videos Using Recurrent Convolutional Network (2018) — 318 citations