| 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 | 2251 | 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 | 1288 | 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 | 723 | 2022 |
| 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 | 571 | 2022 |
| Limits to anatomical accuracy of diffusion tractography using modern approaches KG Schilling, V Nath, C Hansen, P Parvathaneni, J Blaber, Y Gao, ... Neuroimage 185, 1-11, 2019 | 255 | 2019 |
| Synthesized b0 for diffusion distortion correction (Synb0-DisCo) KG Schilling, J Blaber, Y Huo, A Newton, C Hansen, V Nath, AT Shafer, ... Magnetic resonance imaging 64, 62-70, 2019 | 163 | 2019 |
| Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset? KG Schilling, F Rheault, L Petit, CB Hansen, V Nath, FC Yeh, G Girard, ... Neuroimage 243, 118502, 2021 | 154 | 2021 |
| 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 | 138 | 2022 |
| Challenges in diffusion MRI tractography–Lessons learned from international benchmark competitions KG Schilling, A Daducci, K Maier-Hein, C Poupon, JC Houde, V Nath, ... Magnetic resonance imaging 57, 194-209, 2019 | 135 | 2019 |
| PreQual: An automated pipeline for integrated preprocessing and quality assurance of diffusion weighted MRI images LY Cai, Q Yang, CB Hansen, V Nath, K Ramadass, GW Johnson, ... Magnetic resonance in medicine 86 (1), 456-470, 2021 | 97 | 2021 |
| 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 | 87 | 2020 |
| Monai label: A framework for ai-assisted interactive labeling of 3d medical images A Diaz-Pinto, S Alle, V Nath, Y Tang, A Ihsani, M Asad, F Pérez-García, ... Medical Image Analysis 95, 103207, 2024 | 86 | 2024 |
| A fiber coherence index for quality control of B-table orientation in diffusion MRI scans KG Schilling, FC Yeh, V Nath, C Hansen, O Williams, S Resnick, ... Magnetic resonance imaging 58, 82-89, 2019 | 84 | 2019 |
| Cross-scanner and cross-protocol multi-shell diffusion MRI data harmonization: Algorithms and results L Ning, E Bonet-Carne, F Grussu, F Sepehrband, E Kaden, J Veraart, ... Neuroimage 221, 117128, 2020 | 73 | 2020 |
| High-resolution 3D abdominal segmentation with random patch network fusion Y Tang, R Gao, HH Lee, S Han, Y Chen, D Gao, V Nath, C Bermudez, ... Medical image analysis 69, 101894, 2021 | 65 | 2021 |
| Swinunetr-v2: Stronger swin transformers with stagewise convolutions for 3d medical image segmentation Y He, V Nath, D Yang, Y Tang, A Myronenko, D Xu International Conference on Medical Image Computing and Computer-Assisted …, 2023 | 63 | 2023 |
| Tractography reproducibility challenge with empirical data (TraCED): the 2017 ISMRM diffusion study group challenge V Nath, KG Schilling, P Parvathaneni, Y Huo, JA Blaber, AE Hainline, ... Journal of Magnetic Resonance Imaging 51 (1), 234-249, 2020 | 52 | 2020 |
| Fair federated medical image segmentation via client contribution estimation M Jiang, HR Roth, W Li, D Yang, C Zhao, V Nath, D Xu, Q Dou, Z Xu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 46 | 2023 |
| Deep learning reveals untapped information for local white-matter fiber reconstruction in diffusion-weighted MRI V Nath, KG Schilling, P Parvathaneni, CB Hansen, AE Hainline, Y Huo, ... Magnetic resonance imaging 62, 220-227, 2019 | 45 | 2019 |
| Inter-scanner harmonization of high angular resolution DW-MRI using null space deep learning V Nath, P Parvathaneni, CB Hansen, AE Hainline, C Bermudez, ... Computational Diffusion MRI: International MICCAI Workshop, Granada, Spain …, 2019 | 37 | 2019 |
Bennett Allan LandmanElectrical and Computer Engineering, Vanderbilt University在 vanderbilt.edu 的电子邮件经过验证