| Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, ... arXiv preprint arXiv:1811.02629, 2018 | 2333 | 2018 |
| Automated cardiovascular magnetic resonance image analysis with fully convolutional networks W Bai, M Sinclair, G Tarroni, O Oktay, M Rajchl, G Vaillant, AM Lee, ... Journal of cardiovascular magnetic resonance 20 (1), 65, 2018 | 756 | 2018 |
| Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation K Kamnitsas, W Bai, E Ferrante, S McDonagh, M Sinclair, N Pawlowski, ... Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2018 | 602 | 2018 |
| Semi-supervised learning for network-based cardiac MR image segmentation W Bai, O Oktay, M Sinclair, H Suzuki, M Rajchl, G Tarroni, B Glocker, ... Medical Image Computing and Computer-Assisted Intervention− MICCAI 2017 …, 2017 | 522 | 2017 |
| Deepcut: Object segmentation from bounding box annotations using convolutional neural networks M Rajchl, MCH Lee, O Oktay, K Kamnitsas, J Passerat-Palmbach, W Bai, ... IEEE transactions on medical imaging 36 (2), 674-683, 2016 | 493 | 2016 |
| Metric learning with spectral graph convolutions on brain connectivity networks SI Ktena, S Parisot, E Ferrante, M Rajchl, M Lee, B Glocker, D Rueckert NeuroImage 169, 431-442, 2018 | 386 | 2018 |
| 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 (1), 813696, 2015 | 297 | 2015 |
| Right ventricle segmentation from cardiac MRI: a collation study C Petitjean, MA Zuluaga, W Bai, JN Dacher, D Grosgeorge, J Caudron, ... Medical image analysis 19 (1), 187-202, 2015 | 272 | 2015 |
| Distance metric learning using graph convolutional networks: Application to functional brain networks SI Ktena, S Parisot, E Ferrante, M Rajchl, M Lee, B Glocker, D Rueckert Medical Image Computing and Computer Assisted Intervention− MICCAI 2017 …, 2017 | 242 | 2017 |
| Multi-modal learning from unpaired images: Application to multi-organ segmentation in CT and MRI VV Valindria, N Pawlowski, M Rajchl, I Lavdas, EO Aboagye, AG Rockall, ... 2018 IEEE winter conference on applications of computer vision (WACV), 547-556, 2018 | 154 | 2018 |
| Implicit Weight Uncertainty in Neural Networks N Pawlowski, A Brock, MCH Lee, M Rajchl, B Glocker arXiv preprint arXiv:1711.01297, 2018 | 125 | 2018 |
| Accuracy and reproducibility of semi-automated late gadolinium enhancement quantification techniques in patients with hypertrophic cardiomyopathy Y Mikami, L Kolman, SX Joncas, J Stirrat, D Scholl, M Rajchl, CP Lydell, ... Journal of Cardiovascular Magnetic Resonance 16, 1-9, 2014 | 112 | 2014 |
| Prostate Segmentation: An Efficient Convex Optimization Approach with Axial Symmetry Using 3D TRUS and MR Images W Qiu, J Yuan, E Ukwatta, Y Sun, M Rajchl, A Fenster IEEE Transactions on Medical Imaging 33 (4), 947 - 960, 2014 | 103 | 2014 |
| Active Cardiac Sarcoidosis: First Clinical Experience of Simultaneous Positron Emission Tomography–Magnetic Resonance Imaging for the Diagnosis of Cardiac Disease JA White, M Rajchl, J Butler, RT Thompson, FS Prato, G Wisenberg Circulation 127 (22), e639-e641, 2013 | 98 | 2013 |
| Fast fully automatic segmentation of the human placenta from motion corrupted MRI A Alansary, K Kamnitsas, A Davidson, R Khlebnikov, M Rajchl, ... Medical Image Computing and Computer-Assisted Intervention–MICCAI 2016: 19th …, 2016 | 93 | 2016 |
| Dltk: State of the art reference implementations for deep learning on medical images N Pawlowski, SI Ktena, MCH Lee, B Kainz, D Rueckert, B Glocker, ... arXiv preprint arXiv:1711.06853, 2017 | 92 | 2017 |
| Stratified decision forests for accurate anatomical landmark localization in cardiac images O Oktay, W Bai, R Guerrero, M Rajchl, A De Marvao, DP O’Regan, ... IEEE transactions on medical imaging 36 (1), 332-342, 2016 | 89 | 2016 |
| Learning interpretable anatomical features through deep generative models: Application to cardiac remodeling C Biffi, O Oktay, G Tarroni, W Bai, A De Marvao, G Doumou, M Rajchl, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st …, 2018 | 88 | 2018 |
| Left Ventricle Segmentation in MRI via Convex Relaxed Distribution Matching C Nambakhsh, J Yuan, K Punithakumar, A Goela, M Rajchl, TM Peters, ... Medical Image Analysis 17 (8), 1010–-1024, 2013 | 85 | 2013 |
| Unsupervised lesion detection in brain CT using bayesian convolutional autoencoders N Pawlowski, MCH Lee, M Rajchl, S McDonagh, E Ferrante, K Kamnitsas, ... | 83 | 2018 |
Daniel RueckertTechnical University of Munich and Imperial College LondonVerified email at tum.de
