The future of digital health with federated learning N Rieke, J Hancox, W Li, F Milletari, HR Roth, S Albarqouni, S Bakas, ... NPJ digital medicine 3 (1), 1-7, 2020 | 1556 | 2020 |
Unetr: Transformers for 3d medical image segmentation A Hatamizadeh, Y Tang, V Nath, D Yang, A Myronenko, B Landman, ... Proceedings of the IEEE/CVF winter conference on applications of computer …, 2022 | 1511 | 2022 |
The liver tumor segmentation benchmark (lits) P Bilic, P Christ, HB Li, E Vorontsov, A Ben-Cohen, G Kaissis, A Szeskin, ... Medical Image Analysis 84, 102680, 2023 | 1054 | 2023 |
The medical segmentation decathlon M Antonelli, A Reinke, S Bakas, K Farahani, A Kopp-Schneider, ... Nature communications 13 (1), 4128, 2022 | 750 | 2022 |
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 | 750 | 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 | 576 | 2020 |
Privacy-preserving federated brain tumour segmentation W Li, F Milletarì, D Xu, N Rieke, J Hancox, W Zhu, M Baust, Y Cheng, ... Machine Learning in Medical Imaging: 10th International Workshop, MLMI 2019 …, 2019 | 533 | 2019 |
Federated learning for predicting clinical outcomes in patients with COVID-19 I Dayan, HR Roth, A Zhong, A Harouni, A Gentili, AZ Abidin, A Liu, ... Nature medicine 27 (10), 1735-1743, 2021 | 483 | 2021 |
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 | 466 | 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 | 377 | 2020 |
Combo loss: Handling input and output imbalance in multi-organ segmentation SA Taghanaki, Y Zheng, SK Zhou, B Georgescu, P Sharma, D Xu, ... Computerized Medical Imaging and Graphics 75, 24-33, 2019 | 356 | 2019 |
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 | 298 | 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 | 296 | 2017 |
When radiology report generation meets knowledge graph Y Zhang, X Wang, Z Xu, Q Yu, A Yuille, D Xu Proceedings of the AAAI conference on artificial intelligence 34 (07), 12910 …, 2020 | 247 | 2020 |
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 | 232 | 2021 |
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 | 215 | 2020 |
VerSe: a vertebrae labelling and segmentation benchmark for multi-detector CT images A Sekuboyina, ME Husseini, A Bayat, M Löffler, H Liebl, H Li, G Tetteh, ... Medical image analysis 73, 102166, 2021 | 212 | 2021 |
Method and System for Image Registration Using an Intelligent Artificial Agent R Liao, S Miao, P De Tournemire, J Krebs, L Zhang, B Georgescu, S Grbic, ... US Patent App. 15/587,094, 2017 | 191 | 2017 |
Federated learning for breast density classification: A real-world implementation HR Roth, K Chang, P Singh, N Neumark, W Li, V Gupta, S Gupta, L Qu, ... Domain Adaptation and Representation Transfer, and Distributed and …, 2020 | 186 | 2020 |
3d anisotropic hybrid network: Transferring convolutional features from 2d images to 3d anisotropic volumes S Liu, D Xu, SK Zhou, O Pauly, S Grbic, T Mertelmeier, J Wicklein, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st …, 2018 | 172 | 2018 |