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José Bernal
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2020 – today
- 2024
- [j11]Jennifer M. J. Waymont, Maria del C. Valdés Hernández
, José Bernal, Roberto Duarte Coello, Rosalind Brown, Francesca M. Chappell, Lucia Ballerini, Joanna M. Wardlaw:
Systematic review and meta-analysis of automated methods for quantifying enlarged perivascular spaces in the brain. NeuroImage 297: 120685 (2024) - 2023
- [j10]Aditya Sai Ram Nemali
Gaussian Process-based prediction of memory performance and biomarker status in ageing and Alzheimer's disease - A systematic model evaluation. Medical Image Anal. 90: 102913 (2023) - 2021
- [j9]José Bernal, Maria del C. Valdés Hernández
A four-dimensional computational model of dynamic contrast-enhanced magnetic resonance imaging measurement of subtle blood-brain barrier leakage. NeuroImage 230: 117786 (2021) - [j8]José Bernal
Generating Longitudinal Atrophy Evaluation Datasets on Brain Magnetic Resonance Images Using Convolutional Neural Networks and Segmentation Priors. Neuroinformatics 19(3): 477-492 (2021) - [c4]José Bernal
Selective Motion Artefact Reduction via Radiomics and k-space Reconstruction for Improving Perivascular Space Quantification in Brain Magnetic Resonance Imaging. MIUA 2021: 151-164 - 2020
- [b1]José Bernal:
Deep learning for atrophy quantification in brain magnetic resonance imaging. University of Girona, Spain, 2020 - [j7]Albert Clérigues
Acute and sub-acute stroke lesion segmentation from multimodal MRI. Comput. Methods Programs Biomed. 194: 105521 (2020) - [j6]José Bernal
Examining the Relationship between Semiquantitative Methods Analysing Concentration-Time and Enhancement-Time Curves from Dynamic-Contrast Enhanced Magnetic Resonance Imaging and Cerebrovascular Dysfunction in Small Vessel Disease. J. Imaging 6(6): 43 (2020) - [c3]José Bernal
A Framework for Jointly Assessing and Reducing Imaging Artefacts Automatically Using Texture Analysis and Total Variation Optimisation for Improving Perivascular Spaces Quantification in Brain Magnetic Resonance Imaging. MIUA 2020: 171-183
2010 – 2019
- 2019
- [j5]José Bernal
Quantitative Analysis of Patch-Based Fully Convolutional Neural Networks for Tissue Segmentation on Brain Magnetic Resonance Imaging. IEEE Access 7: 89986-90002 (2019) - [j4]José Bernal
Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review. Artif. Intell. Medicine 95: 64-81 (2019) - [j3]Albert Clérigues
Acute ischemic stroke lesion core segmentation in CT perfusion images using fully convolutional neural networks. Comput. Biol. Medicine 115 (2019) - [c2]José Bernal
Analysis of Spatial Spectral Features of Dynamic Contrast-Enhanced Brain Magnetic Resonance Images for Studying Small Vessel Disease. MIUA 2019: 282-293 - 2018
- [j2]Claudia Mazo
Transfer learning for classification of cardiovascular tissues in histological images. Comput. Methods Programs Biomed. 165: 69-76 (2018) - [j1]Kaisar Kushibar
Automated sub-cortical brain structure segmentation combining spatial and deep convolutional features. Medical Image Anal. 48: 177-186 (2018) - [i6]José Bernal, Kaisar Kushibar, Mariano Cabezas, Sergi Valverde, Arnau Oliver, Xavier Lladó:
Quantitative analysis of patch-based fully convolutional neural networks for tissue segmentation on brain magnetic resonance imaging. CoRR abs/1801.06457 (2018) - [i5]Mariano Cabezas, Sergi Valverde, Sandra González-Villà
Survival prediction using ensemble tumor segmentation and transfer learning. CoRR abs/1810.04274 (2018) - [i4]Albert Clérigues, Sergi Valverde, José Bernal, Jordi Freixenet, Arnau Oliver, Xavier Lladó:
SUNet: a deep learning architecture for acute stroke lesion segmentation and outcome prediction in multimodal MRI. CoRR abs/1810.13304 (2018) - [i3]Spyridon Bakas, Mauricio Reyes, András Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, Russell Takeshi Shinohara, Christoph Berger, Sung Min Ha, Martin Rozycki, Marcel Prastawa, Esther Alberts, Jana Lipková, John B. Freymann, Justin S. Kirby, Michel Bilello, Hassan M. Fathallah-Shaykh, Roland Wiest, Jan Kirschke, Benedikt Wiestler, Rivka R. Colen, Aikaterini Kotrotsou, Pamela LaMontagne, Daniel S. Marcus, Mikhail Milchenko, Arash Nazeri, Marc-André Weber, Abhishek Mahajan, Ujjwal Baid, Dongjin Kwon, Manu Agarwal, Mahbubul Alam, Alberto Albiol, Antonio Albiol
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge. CoRR abs/1811.02629 (2018) - 2017
- [i2]Kaisar Kushibar, Sergi Valverde, Sandra González-Villà, José Bernal, Mariano Cabezas, Arnau Oliver, Xavier Lladó:
Automated sub-cortical brain structure segmentation combining spatial and deep convolutional features. CoRR abs/1709.09075 (2017) - [i1]José Bernal, Kaisar Kushibar, Daniel S. Asfaw, Sergi Valverde, Arnau Oliver, Robert Marti, Xavier Lladó:
Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review. CoRR abs/1712.03747 (2017) - 2014
- [c1]José Bernal
Evaluating Robustness of Template Matching Algorithms as a Multi-objective Optimisation Problem. CIARP 2014: 30-37
Coauthor Index
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