| Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis V Cheplygina, M de Bruijne, JPW Pluim Medical image analysis 54, 280-296, 2019 | 779 | 2019 |
| Evaluation of registration methods on thoracic CT: the EMPIRE10 challenge K Murphy, B Van Ginneken, JM Reinhardt, S Kabus, K Ding, X Deng, ... IEEE transactions on medical imaging 30 (11), 1901-1920, 2011 | 496 | 2011 |
| Quantitative analysis of pulmonary emphysema using local binary patterns L Sørensen, SB Shaker, M De Bruijne Medical Imaging, IEEE Transactions on 29 (2), 559-569, 2010 | 435 | 2010 |
| Medical Image Computing and Compter-Assisted Intervention (MICCAI) M de Bruijne, PC Cattin, S Cotin, N Padoy, S Speidel, Y Zheng, C Essert Springer 12904, 519-528, 2021 | 389* | 2021 |
| A genome-wide association study identifies five loci influencing facial morphology in Europeans F Liu, F Van Der Lijn, C Schurmann, G Zhu, MM Chakravarty, PG Hysi, ... PLoS Genet 8 (9), e1002932, 2012 | 381 | 2012 |
| Machine learning approaches in medical image analysis: From detection to diagnosis M de Bruijne Medical Image Analysis 33, 94-97, 2016 | 340 | 2016 |
| Extraction of Airways from CT (EXACT’09) P Lo, B van Ginneken, J Reinhardt, T Y., P de Jong, B Irving, C Fetita, ... IEEE Transactions on Medical Imaging 31 (11), 2093-2107, 2012 | 285 | 2012 |
| Transfer learning improves supervised image segmentation across imaging protocols A Van Opbroek, MA Ikram, MW Vernooij, M De Bruijne IEEE transactions on medical imaging 34 (5), 1018-1030, 2014 | 283 | 2014 |
| Genetic landscape of chronic obstructive pulmonary disease identifies heterogeneous cell-type and phenotype associations P Sakornsakolpat, D Prokopenko, M Lamontagne, NF Reeve, AL Guyatt, ... Nature genetics 51 (3), 494-505, 2019 | 275 | 2019 |
| MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans AM Mendrik, KL Vincken, HJ Kuijf, M Breeuwer, WH Bouvy, J de Bresser, ... Computational Intelligence and Neuroscience, 2015 | 272 | 2015 |
| PRAGMA-CF. A Quantitative Structural Lung Disease Computed Tomography Outcome in Young Children with Cystic Fibrosis T Rosenow, MCJ Oudraad, CP Murray, L Turkovic, W Kuo, M de Bruijne, ... American journal of respiratory and critical care medicine 191 (10), 1158-1165, 2015 | 210 | 2015 |
| 2D–3D shape reconstruction of the distal femur from stereo X-ray imaging using statistical shape models N Baka, BL Kaptein, M de Bruijne, T van Walsum, JE Giphart, WJ Niessen, ... Medical image analysis 15 (6), 840-850, 2011 | 208 | 2011 |
| Scalable kernels for graphs with continuous attributes A Feragen, N Kasenburg, J Petersen, M de Bruijne, K Borgwardt Advances in neural information processing systems 26, 2013 | 200 | 2013 |
| Adapting active shape models for 3D segmentation of tubular structures in medical images M de Bruijne, B van Ginneken, MA Viergever, WJ Niessen Information Processing in Medical Imaging: 18th International Conference …, 2003 | 200 | 2003 |
| Semi-supervised medical image segmentation via learning consistency under transformations G Bortsova, F Dubost, L Hogeweg, I Katramados, M De Bruijne Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd …, 2019 | 180 | 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 | 171 | 2016 |
| Gray matter age prediction as a biomarker for risk of dementia J Wang, MJ Knol, A Tiulpin, F Dubost, M de Bruijne, MW Vernooij, ... Proceedings of the National Academy of Sciences 116 (42), 21213-21218, 2019 | 160 | 2019 |
| Vessel-guided airway tree segmentation: A voxel classification approach P Lo, J Sporring, H Ashraf, JJH Pedersen, M de Bruijne Medical image analysis 14 (4), 527-538, 2010 | 159 | 2010 |
| Interactive segmentation of abdominal aortic aneurysms in CTA images M De Bruijne, B Van Ginneken, MA Viergever, WJ Niessen Medical Image Analysis 8, 127–138, 2004 | 153 | 2004 |
| Multi-task attention-based semi-supervised learning for medical image segmentation S Chen, G Bortsova, A García-Uceda Juárez, G Van Tulder, M De Bruijne Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd …, 2019 | 138 | 2019 |
Wiro NiessenProfessor in Biomedical Image Analysis, Erasmus MC/TU DelftVerified email at erasmusmc.nl
