| 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 | 2067 | 2018 |
| A state of the art in structured light patterns for surface profilometry J Salvi, S Fernandez, T Pribanic, X Llado Pattern recognition 43 (8), 2666-2680, 2010 | 1062 | 2010 |
| A review of atlas-based segmentation for magnetic resonance brain images M Cabezas, A Oliver, X Lladó, J Freixenet, MB Cuadra Computer methods and programs in biomedicine 104 (3), e158-e177, 2011 | 576 | 2011 |
| Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review J Bernal, K Kushibar, DS Asfaw, S Valverde, A Oliver, R Martí, X Lladó Artificial intelligence in medicine 95, 64-81, 2019 | 481 | 2019 |
| The SLAM problem: a survey J Aulinas, Y Petillot, J Salvi, X Lladó Artificial Intelligence Research and Development, 363-371, 2008 | 468 | 2008 |
| Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach S Valverde, M Cabezas, E Roura, S González-Villà, D Pareto, ... NeuroImage 155, 159-168, 2017 | 395 | 2017 |
| Standardized assessment of automatic segmentation of white matter hyperintensities and results of the WMH segmentation challenge HJ Kuijf, JM Biesbroek, J De Bresser, R Heinen, S Andermatt, M Bento, ... IEEE transactions on medical imaging 38 (11), 2556-2568, 2019 | 310 | 2019 |
| Segmentation of multiple sclerosis lesions in brain MRI: a review of automated approaches X Lladó, A Oliver, M Cabezas, J Freixenet, JC Vilanova, A Quiles, L Valls, ... Information Sciences 186 (1), 164-185, 2012 | 307 | 2012 |
| Objective evaluation of multiple sclerosis lesion segmentation using a data management and processing infrastructure O Commowick, A Istace, M Kain, B Laurent, F Leray, M Simon, SC Pop, ... Scientific reports 8 (1), 13650, 2018 | 272 | 2018 |
| A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography images S Ghose, A Oliver, R Martí, X Lladó, JC Vilanova, J Freixenet, J Mitra, ... Computer methods and programs in biomedicine 108 (1), 262-287, 2012 | 265 | 2012 |
| Automatic mass detection in mammograms using deep convolutional neural networks R Agarwal, O Diaz, X Lladó, MH Yap, R Martí Journal of Medical Imaging 6 (3), 031409-031409, 2019 | 196 | 2019 |
| False positive reduction in mammographic mass detection using local binary patterns A Oliver, X Lladó, J Freixenet, J Martí Medical Image Computing and Computer-Assisted Intervention–MICCAI 2007: 10th …, 2007 | 162 | 2007 |
| One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks S Valverde, M Salem, M Cabezas, D Pareto, JC Vilanova, ... NeuroImage: Clinical 21, 101638, 2019 | 155 | 2019 |
| Automatic microcalcification and cluster detection for digital and digitised mammograms A Oliver, A Torrent, X Lladó, M Tortajada, L Tortajada, M Sentís, ... Knowledge-Based Systems 28, 68-75, 2012 | 143 | 2012 |
| A review on brain structures segmentation in magnetic resonance imaging S González-Villà, A Oliver, S Valverde, L Wang, R Zwiggelaar, X Lladó Artificial intelligence in medicine 73, 45-69, 2016 | 139 | 2016 |
| Deep learning for mass detection in full field digital mammograms R Agarwal, O Diaz, MH Yap, X Lladó, R Marti Computers in biology and medicine 121, 103774, 2020 | 130 | 2020 |
| A qualitative review on 3D coarse registration methods Y Diez, F Roure, X Lladó, J Salvi ACM Computing Surveys (CSUR) 47 (3), 1-36, 2015 | 127 | 2015 |
| Automated sub-cortical brain structure segmentation combining spatial and deep convolutional features K Kushibar, S Valverde, S Gonzalez-Villa, J Bernal, M Cabezas, A Oliver, ... Medical image analysis 48, 177-186, 2018 | 125 | 2018 |
| A textural approach for mass false positive reduction in mammography X Lladó, A Oliver, J Freixenet, R Martí, J Martí Computerized Medical Imaging and Graphics 33 (6), 415-422, 2009 | 125 | 2009 |
| Improving the detection of autism spectrum disorder by combining structural and functional MRI information M Rakić, M Cabezas, K Kushibar, A Oliver, X Lladó NeuroImage: Clinical 25, 102181, 2020 | 120 | 2020 |
Arnau OliverUniversitat de GironaVerified email at eia.udg.edu
