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Nanqing Dong(董楠卿)
Nanqing Dong(董楠卿)
Shanghai Artificial Intelligence Laboratory; University of Oxford
Verified email at cs.ox.ac.uk - Homepage
Title
Cited by
Cited by
Year
Few-shot semantic segmentation with prototype learning
N Dong, EP Xing
British Machine Vision Conference, 2018
4502018
Scan: Structure correcting adversarial network for organ segmentation in chest x-rays
W Dai, N Dong, Z Wang, X Liang, H Zhang, EP Xing
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical …, 2018
2582018
Unsupervised domain adaptation for automatic estimation of cardiothoracic ratio
N Dong, M Kampffmeyer, X Liang, Z Wang, W Dai, E Xing
International Conference on Medical Image Computing and Computer-Assisted …, 2018
1072018
Edge computing with artificial intelligence: A machine learning perspective
H Hua, Y Li, T Wang, N Dong, W Li, J Cao
ACM Computing Surveys 55 (9), 1-35, 2022
972022
Data-driven Dynamical Control for Bottom-up Energy Internet System
H Hua, Z Qin, N Dong, Y Qin, M Ye, Z Wang, X Chen, J Cao
IEEE Transactions on Sustainable Energy 13 (1), 315-327, 2022
952022
Toward Understanding the Impact of Staleness in Distributed Machine Learning
W Dai, Y Zhou, N Dong, H Zhang, E Xing
International Conference on Learning Representations, 2019
752019
ConnNet: A long-range relation-aware pixel-connectivity network for salient segmentation
M Kampffmeyer, N Dong, X Liang, Y Zhang, EP Xing
IEEE Transactions on Image Processing 28 (5), 2518-2529, 2018
612018
Domain Adaption in One-Shot Learning
N Dong, EP Xing
European Conference on Machine Learning, 573-588, 2018
492018
Classification of breast cancer histopathological images using convolutional neural networks with hierarchical loss and global pooling
Z Wang, N Dong, W Dai, SD Rosario, EP Xing
International Conference on Image Analysis and Recognition, 745-753, 2018
442018
Neural architecture search for adversarial medical image segmentation
N Dong, M Xu, X Liang, Y Jiang, W Dai, E Xing
International Conference on Medical Image Computing and Computer Assisted …, 2019
432019
Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-Slide Images
N Dong, M Kampffmeyer, X Liang, Z Wang, W Dai, E Xing
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical …, 2018
432018
Federated Contrastive Learning for Decentralized Unlabeled Medical Images
N Dong, I Voiculescu
International Conference on Medical Image Computing and Computer-Assisted …, 2021
422021
Ellipse detection of optic disc-and-cup boundary in fundus images
Z Wang, N Dong, SD Rosario, M Xu, P Xie, EP Xing
IEEE International Symposium on Biomedical Imaging, 601-604, 2019
312019
Self-Supervised Multi-Task Representation Learning for Sequential Medical Images
N Dong, M Kampffmeyer, I Voiculescu
European Conference on Machine Learning, 779-794, 2021
182021
Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy Images
N Dong, M Maggioni, Y Yang, E Pérez-Pellitero, A Leonardis, ...
International Joint Conference on Artificial Intelligence, 2930-2936, 2022
17*2022
Adversarial Domain Adaptation Being Aware of Class Relationships
Z Wang, B Jing, Y Ni, N Dong, P Xie, EP Xing
European Conference on Artificial Intelligence, 1579-1586, 2020
172020
Computationally-efficient vision transformer for medical image semantic segmentation via dual pseudo-label supervision
Z Wang, N Dong, I Voiculescu
IEEE International Conference on Image Processing (ICIP), 1961-1965, 2022
162022
Towards Robust Partially Supervised Multi-Structure Medical Image Segmentation on Small-Scale Data
N Dong, M Kampffmeyer, X Liang, M Xu, I Voiculescu, EP Xing
Applied Soft Computing 114, 108074, 2022
132022
Federated partially supervised learning with limited decentralized medical images
N Dong, M Kampffmeyer, I Voiculescu, E Xing
IEEE Transactions on Medical Imaging 42 (7), 1944-1954, 2022
82022
Negational Symmetry of Quantum Neural Networks for Binary Pattern Classification
N Dong, M Kampffmeyer, I Voiculescu, E Xing
Pattern Recognition 129, 108750, 2022
62022
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