Holger R. Roth
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Системата не може да изпълни операцията сега. Опитайте отново по-късно.
Статии 1–20