Jing Qin

Computer Vision and Pattern Recognition · Medical Image Anal. · 2015–2021

A researcher in the Science corpus: 16 papers; trusts 831 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 317 citations into it; #11,325 of 21,575 by trust here, #3,404 by citations
• Artificial Intelligence — 243 citations into it; #17,495 of 35,345 by trust here, #5,153 by citations
• Radiology, Nuclear Medicine and Imaging — 93 citations into it; #1,224 of 3,265 by trust here, #403 by citations
• Model Architectures — 78 citations into it
• Representation & Self-Supervised Learning — 43 citations into it
• Biomedical Engineering — 36 citations into it; #10,377 of 10,734 by trust here, #3,764 by citations
• Optimization & Training — 33 citations into it
• Biophysics — 23 citations into it

Whom Jing Qin trusts overall

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

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

Who trusts Jing Qin overall

1. Hao Chen
2. Qi-Yan Dou
3. Mingxia Liu
4. Geert J. S. Litjens
5. Lequan Yu
6. Mufti Mahmud
7. Yutong Xie
8. Berkman Sahiner
9. José Bernal
10. Balázs Harangi

Papers from Jing Qin's perspective

Ordered by how strongly the researchers Jing Qin 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. 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

• 3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes (2016) — 370 citations
• Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans (2016) — 730 citations
• Volumetric ConvNets with Mixed Residual Connections for Automated Prostate Segmentation from 3D MR Images (2017) — 358 citations
• R³Net: Recurrent Residual Refinement Network for Saliency Detection (2018) — 518 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
• Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation (2020) — 398 citations
• SV-RCNet: Workflow Recognition From Surgical Videos Using Recurrent Convolutional Network (2018) — 318 citations
• Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection (2017) — 506 citations
• Application of Deep Learning in Automated Analysis of Molecular Images in Cancer: A Survey (2017) — 52 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
• Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks (2017) — 1,025 citations
• Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks (2016) — 621 citations
• Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks (2015) — 348 citations