| Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning HC Shin, HR Roth, M Gao, L Lu, Z Xu, I Nogues, J Yao, D Mollura, ... IEEE transactions on medical imaging 35 (5), 1285-1298, 2016 | 5952 | 2016 |
| 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 | 1545 | 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 | 1452 | 2022 |
| Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation HR Roth, L Lu, A Farag, HC Shin, J Liu, EB Turkbey, RM Summers Medical Image Computing and Computer-Assisted Intervention--MICCAI 2015 …, 2015 | 896 | 2015 |
| 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 | 735 | 2021 |
| Improving computer-aided detection using convolutional neural networks and random view aggregation HR Roth, L Lu, J Liu, J Yao, A Seff, K Cherry, L Kim, RM Summers IEEE transactions on medical imaging 35 (5), 1170-1181, 2015 | 731 | 2015 |
| A new 2.5 D representation for lymph node detection using random sets of deep convolutional neural network observations HR Roth, L Lu, A Seff, KM Cherry, J Hoffman, S Wang, J Liu, E Turkbey, ... Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014: 17th …, 2014 | 685 | 2014 |
| 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 | 574 | 2020 |
| 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 | 479 | 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 | 454 | 2022 |
| Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation HR Roth, L Lu, N Lay, AP Harrison, A Farag, A Sohn, RM Summers Medical image analysis 45, 94-107, 2018 | 430 | 2018 |
| 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 | 375 | 2020 |
| Artificial intelligence-assisted polyp detection for colonoscopy: initial experience M Misawa, S Kudo, Y Mori, T Cho, S Kataoka, A Yamauchi, Y Ogawa, ... Gastroenterology 154 (8), 2027-2029. e3, 2018 | 357 | 2018 |
| An application of cascaded 3D fully convolutional networks for medical image segmentation HR Roth, H Oda, X Zhou, N Shimizu, Y Yang, Y Hayashi, M Oda, ... Computerized Medical Imaging and Graphics 66, 90-99, 2018 | 313 | 2018 |
| Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks M Gao, U Bagci, L Lu, A Wu, M Buty, HC Shin, H Roth, GZ Papadakis, ... Computer Methods in Biomechanics and Biomedical Engineering: Imaging …, 2018 | 290 | 2018 |
| 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 | 279 | 2022 |
| Anatomy-specific classification of medical images using deep convolutional nets HR Roth, CT Lee, HC Shin, A Seff, L Kim, J Yao, L Lu, RM Summers 2015 IEEE 12th international symposium on biomedical imaging (ISBI), 101-104, 2015 | 232 | 2015 |
| 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 | 231 | 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 | 211 | 2020 |
| A bottom-up approach for pancreas segmentation using cascaded superpixels and (deep) image patch labeling A Farag, L Lu, HR Roth, J Liu, E Turkbey, RM Summers IEEE Transactions on image processing 26 (1), 386-399, 2016 | 203 | 2016 |
Daguang XuSenior Research Manager at NVIDIA在 nvidia.com 的电子邮件经过验证
