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| Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images A Hatamizadeh, V Nath, Y Tang, D Yang, HR Roth, D Xu International MICCAI brainlesion workshop, 272-284, 2021 | 979 | 2021 |
| Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets SA Harmon, TH Sanford, S Xu, EB Turkbey, H Roth, Z Xu, D Yang, ... Nature communications 11 (1), 4080, 2020 | 607 | 2020 |
| Self-supervised pre-training of swin transformers for 3d medical image analysis Y Tang, D Yang, W Li, HR Roth, B Landman, D Xu, V Nath, ... Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022 | 586 | 2022 |
| Generalizing deep learning for medical image segmentation to unseen domains via deep stacked transformation L Zhang, X Wang, D Yang, T Sanford, S Harmon, B Turkbey, BJ Wood, ... IEEE transactions on medical imaging 39 (7), 2531-2540, 2020 | 427 | 2020 |
| Monai: An open-source framework for deep learning in healthcare MJ Cardoso, W Li, R Brown, N Ma, E Kerfoot, Y Wang, B Murrey, ... arXiv preprint arXiv:2211.02701, 2022 | 415 | 2022 |
| Automatic liver segmentation using an adversarial image-to-image network D Yang, D Xu, SK Zhou, B Georgescu, M Chen, S Grbic, D Metaxas, ... Medical Image Computing and Computer Assisted Intervention− MICCAI 2017 …, 2017 | 300 | 2017 |
| Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan D Yang, Z Xu, W Li, A Myronenko, HR Roth, S Harmon, S Xu, B Turkbey, ... Medical image analysis 70, 101992, 2021 | 250 | 2021 |
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| Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation Y Xia, D Yang, Z Yu, F Liu, J Cai, L Yu, Z Zhu, D Xu, A Yuille, H Roth Medical image analysis 65, 101766, 2020 | 226 | 2020 |
| 3d semi-supervised learning with uncertainty-aware multi-view co-training Y Xia, F Liu, D Yang, J Cai, L Yu, Z Zhu, D Xu, A Yuille, H Roth Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2020 | 175 | 2020 |
| C2fnas: Coarse-to-fine neural architecture search for 3d medical image segmentation Q Yu, D Yang, H Roth, Y Bai, Y Zhang, AL Yuille, D Xu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 163 | 2020 |
| Automated anatomical landmark detection ondistal femur surface using convolutional neural network D Yang, S Zhang, Z Yan, C Tan, K Li, D Metaxas 2015 IEEE 12th international symposium on biomedical imaging (ISBI), 17-21, 2015 | 136 | 2015 |
| V-NAS: Neural architecture search for volumetric medical image segmentation Z Zhu, C Liu, D Yang, A Yuille, D Xu 2019 International conference on 3d vision (3DV), 240-248, 2019 | 130 | 2019 |
| Automatic vertebra labeling in large-scale 3D CT using deep image-to-image network with message passing and sparsity regularization D Yang, T Xiong, D Xu, Q Huang, D Liu, SK Zhou, Z Xu, JH Park, M Chen, ... Information Processing in Medical Imaging: 25th International Conference …, 2017 | 130 | 2017 |
| Proceedings of the IEEE/CVF winter conference on applications of computer vision A Hatamizadeh, Y Tang, V Nath, D Yang, A Myronenko, B Landman IEEE, 2022 | 103 | 2022 |
| Dints: Differentiable neural network topology search for 3d medical image segmentation Y He, D Yang, H Roth, C Zhao, D Xu Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 100 | 2021 |
| MRI reconstruction via cascaded channel-wise attention network Q Huang, D Yang, P Wu, H Qu, J Yi, D Metaxas 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019 …, 2019 | 99 | 2019 |
| Diminishing uncertainty within the training pool: Active learning for medical image segmentation V Nath, D Yang, BA Landman, D Xu, HR Roth IEEE Transactions on Medical Imaging 40 (10), 2534-2547, 2020 | 74 | 2020 |
Daguang XuSenior Research Manager at NVIDIAПотвърден имейл адрес: nvidia.com
