| Anatomically constrained neural networks (ACNNs): application to cardiac image enhancement and segmentation O Oktay, E Ferrante, K Kamnitsas, M Heinrich, W Bai, J Caballero, ... arXiv preprint arXiv:1705.08302v3, 2017 | 839 | 2017 |
| Disease prediction using graph convolutional networks: application to autism spectrum disorder and Alzheimer’s disease S Parisot, SI Ktena, E Ferrante, M Lee, R Guerrero, B Glocker, D Rueckert Medical image analysis 48, 117-130, 2018 | 648 | 2018 |
| Gan augmentation: Augmenting training data using generative adversarial networks C Bowles, L Chen, R Guerrero, P Bentley, R Gunn, A Hammers, ... arXiv preprint arXiv:1810.10863, 2018 | 636 | 2018 |
| Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge EE Bron, M Smits, WM Van Der Flier, H Vrenken, F Barkhof, P Scheltens, ... NeuroImage 111, 562-579, 2015 | 392 | 2015 |
| Multi-Input Cardiac Image Super-Resolution using Convolutional Neural Networks O Oktay, W Bai, M Lee, R Guerrero, K Kamnitsas, J Caballero, ... Medical Image Computing and Computer-Assisted Intervention–MICCAI 2016, 2016 | 295 | 2016 |
| Spectral Graph Convolutions on Population Graphs for Disease Prediction S Parisot, SI Ktena, E Ferrante, M Lee, RG Moreno, B Glocker, D Rueckert International Conference on Medical Image Computing and Computer-Assisted …, 2017 | 284* | 2017 |
| White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks R Guerrero, C Qin, O Oktay, C Bowles, L Chen, R Joules, R Wolz, ... NeuroImage: Clinical, 2017 | 246 | 2017 |
| Multiple instance learning for classification of dementia in brain MRI T Tong, R Wolz, Q Gao, R Guerrero, JV Hajnal, D Rueckert, ... Medical image analysis 18 (5), 808-818, 2014 | 244 | 2014 |
| Differential diagnosis of neurodegenerative diseases using structural MRI data J Koikkalainen, H Rhodius-Meester, A Tolonen, F Barkhof, B Tijms, ... NeuroImage: Clinical 11, 435-449, 2016 | 203 | 2016 |
| Structural brain imaging in Alzheimer’s disease and mild cognitive impairment: biomarker analysis and shared morphometry database C Ledig, A Schuh, R Guerrero, RA Heckemann, D Rueckert Scientific reports 8 (1), 11258, 2018 | 196 | 2018 |
| A Novel Grading Biomarker for the Prediction of Conversion from Mild Cognitive Impairment to Alzheimer's Disease T Tong, Q Gao, R Guerrero, C Ledig, L Chen, D Rueckert Transactions on Biomedical Engineering, 2016 | 165 | 2016 |
| 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 | 88 | 2016 |
| Automatic view planning with multi-scale deep reinforcement learning agents A Alansary, LL Folgoc, G Vaillant, O Oktay, Y Li, W Bai, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st …, 2018 | 69 | 2018 |
| Manifold population modeling as a neuro-imaging biomarker: application to ADNI and ADNI-GO R Guerrero, R Wolz, AW Rao, D Rueckert, ... NeuroImage 94, 275-286, 2014 | 65 | 2014 |
| Instantiated mixed effects modeling of Alzheimer's disease markers R Guerrero, A Schmidt-Richberg, C Ledig, T Tong, R Wolz, D Rueckert, ... NeuroImage, 2016 | 62 | 2016 |
| Brain lesion segmentation through image synthesis and outlier detection C Bowles, C Qin, R Guerrero, R Gunn, A Hammers, DA Dickie, ... NeuroImage: Clinical 16, 643-658, 2017 | 59 | 2017 |
| Manifold alignment and transfer learning for classification of Alzheimer’s disease R Guerrero, C Ledig, D Rueckert International Workshop on Machine Learning in Medical Imaging, 77-84, 2014 | 54 | 2014 |
| Five-class differential diagnostics of neurodegenerative diseases using random undersampling boosting T Tong, C Ledig, R Guerrero, A Schuh, J Koikkalainen, A Tolonen, ... NeuroImage: Clinical 15, 613-624, 2017 | 51 | 2017 |
| Learning Biomarker Models for Progression Estimation of Alzheimer’s Disease A Schmidt-Richberg, C Ledig, R Guerrero, H Molina-Abril, A Frangi, ... PLOSOne 11 (4), e0153040, 2016 | 43 | 2016 |
| Pseudo-healthy image synthesis for white matter lesion segmentation C Bowles, C Qin, C Ledig, R Guerrero, R Gunn, A Hammers, E Sakka, ... Simulation and Synthesis in Medical Imaging: First International Workshop …, 2016 | 43 | 2016 |
Daniel RueckertTechnical University of Munich and Imperial College LondonВерификована је имејл адреса на tum.de