| Theano: A Python framework for fast computation of mathematical expressions R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ... arXiv e-prints, arXiv: 1605.02688, 2016 | 1189* | 2016 |
| Multi-task attention-based semi-supervised learning for medical image segmentation S Chen, G Bortsova, A García-Uceda Juárez, G Tulder, M Bruijne International Conference on Medical Image Computing and Computer-Assisted …, 2019 | 183 | 2019 |
| Combining generative and discriminative representation learning for lung CT analysis with convolutional restricted Boltzmann machines G van Tulder, M de Bruijne IEEE Transactions on Medical Imaging 35 (5), 1262-1272, 2016 | 183 | 2016 |
| Why does synthesized data improve multi-sequence classification? G van Tulder, M de Bruijne International Conference on Medical Image Computing and Computer-Assisted …, 2015 | 113 | 2015 |
| Multi-view analysis of unregistered medical images using cross-view transformers G van Tulder, Y Tong, E Marchiori International Conference on Medical Image Computing and Computer-Assisted …, 2021 | 69 | 2021 |
| Learning Cross-Modality Representations from Multi-Modal Images G van Tulder, M de Bruijne IEEE Transactions on Medical Imaging, 2018 | 62 | 2018 |
| Weakly supervised object detection with 2D and 3D regression neural networks F Dubost, H Adams, P Yilmaz, G Bortsova, G van Tulder, MA Ikram, ... Medical Image Analysis 65, 101767, 2020 | 50 | 2020 |
| Question classification by weighted combination of lexical, syntactic and semantic features B Loni, G van Tulder, P Wiggers, DMJ Tax, M Loog Text, Speech and Dialogue, 243-250, 2011 | 48 | 2011 |
| An end-to-end approach to segmentation in medical images with CNN and posterior-CRF S Chen, ZS Gamechi, F Dubost, G van Tulder, M de Bruijne Medical Image Analysis 76, 102311, 2022 | 38 | 2022 |
| Learning features for tissue classification with the classification restricted Boltzmann machine G van Tulder, M de Bruijne International MICCAI workshop on medical computer vision, 47-58, 2014 | 35 | 2014 |
| Storing hierarchical data in a database G van Tulder SitePoint Pty. Ltd. http://www.sitepoint.com/article/hierarchical-data-database, 2003 | 22 | 2003 |
| Segmentation of intracranial arterial calcification with deeply supervised residual dropout networks G Bortsova, G van Tulder, F Dubost, T Peng, N Navab, A van der Lugt, ... International Conference on Medical Image Computing and Computer-Assisted …, 2017 | 19 | 2017 |
| Automated Segmentation and Volume Measurement of Intracranial Internal Carotid Artery Calcification at Noncontrast CT G Bortsova, D Bos, F Dubost, MW Vernooij, MK Ikram, G van Tulder, ... Radiology: Artificial Intelligence, e200226, 2021 | 15 | 2021 |
| Diaphragmatic dysfunction in neuromuscular disease, an MRI study L Harlaar, P Ciet, G van Tulder, E Brusse, RGM Timmermans, ... Neuromuscular Disorders 32 (1), 15-24, 2022 | 14 | 2022 |
| Representation learning for cross-modality classification G van Tulder, M de Bruijne MICCAI Workshop on Medical Computer Vision, 2016 | 13 | 2016 |
| Chest MRI to diagnose early diaphragmatic weakness in Pompe disease L Harlaar, P Ciet, G van Tulder, A Pittaro, HA van Kooten, ... Orphanet journal of rare diseases 16 (1), 1-12, 2021 | 11 | 2021 |
| Label Refinement Network from Synthetic Error Augmentation for Medical Image Segmentation S Chen, AG Uceda, J Su, G van Tulder, L Wolff, T van Walsum, ... arXiv preprint arXiv:2209.06353, 2022 | 4 | 2022 |
| Unpaired, unsupervised domain adaptation assumes your domains are already similar G van Tulder, M de Bruijne Medical Image Analysis, 102825, 2023 | 3 | 2023 |
| MRI changes in diaphragmatic motion and curvature in Pompe disease over time L Harlaar, P Ciet, G van Tulder, HA van Kooten, NAME van der Beek, ... European Radiology 32 (12), 8681-8691, 2022 | 2 | 2022 |
| Generating Artificial Artifacts for Motion Artifact Detection in Chest CT G van der Ham, R Latisenko, M Tsiaousis, G van Tulder Simulation and Synthesis in Medical Imaging: 7th International Workshop …, 2022 | 2 | 2022 |
