Arnau Oliver
Cited by
Cited by
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
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
A review of automatic mass detection and segmentation in mammographic images
A Oliver, J Freixenet, J Marti, E Perez, J Pont, ERE Denton, R Zwiggelaar
Medical image analysis 14 (2), 87-110, 2010
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
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
A novel breast tissue density classification methodology
A Oliver, J Freixenet, R Marti, J Pont, E Pérez, ERE Denton, R Zwiggelaar
Ieee transactions on information technology in biomedicine 12 (1), 55-65, 2008
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
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
Breast segmentation with pectoral muscle suppression on digital mammograms
D Raba, A Oliver, J Martí, M Peracaula, J Espunya
Pattern Recognition and Image Analysis: Second Iberian Conference, IbPRIA …, 2005
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
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
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
Modeling and classifying breast tissue density in mammograms
A Bosch, X Munoz, A Oliver, J Marti
2006 IEEE computer society conference on computer vision and pattern …, 2006
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
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
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
Automated detection of multiple sclerosis lesions in serial brain MRI
X Lladó, O Ganiler, A Oliver, R Martí, J Freixenet, L Valls, JC Vilanova, ...
Neuroradiology 54, 787-807, 2012
Comparison of 10 brain tissue segmentation methods using revisited IBSR annotations
S Valverde, A Oliver, M Cabezas, E Roura, X Lladó
Journal of Magnetic Resonance Imaging 41 (1), 93-101, 2015
A toolbox for multiple sclerosis lesion segmentation
E Roura, A Oliver, M Cabezas, S Valverde, D Pareto, JC Vilanova, ...
Neuroradiology 57, 1031-1043, 2015
Topological modeling and classification of mammographic microcalcification clusters
Z Chen, H Strange, A Oliver, ERE Denton, C Boggis, R Zwiggelaar
IEEE transactions on biomedical engineering 62 (4), 1203-1214, 2014
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