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Author
Title
Year
Publication
Volume
Pages
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Debora Gil; Antonio Esteban Lansaque; Sebastian Stefaniga; Mihail Gaianu; Carles Sanchez
Data Augmentation from Sketch
2019
International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging
11840
155-162
Debora Gil; Aura Hernandez-Sabate; Oriol Rodriguez; Josepa Mauri; Petia Radeva
Statistical Strategy for Anisotropic Adventitia Modelling in IVUS
2006
IEEE Transactions on Medical Imaging
25
768-778
Sergio Vera; Debora Gil; Miguel Angel Gonzalez Ballester
Anatomical parameterization for volumetric meshing of the liver
2014
SPIE – Medical Imaging
9036
Debora Gil; Oriol Rodriguez-Leon; Petia Radeva; Josepa Mauri
Myocardial Perfusion Characterization From Contrast Angiography Spectral Distribution
2008
IEEE Transactions on Medical Imaging
27
641-649
Debora Gil; Petia Radeva
Extending anisotropic operators to recover smooth shapes
2005
Computer Vision and Image Understanding
99
110-125
Mariano Vazquez; Ruth Aris; Guillaume Hozeaux; R.Aubry; P.Villar;Jaume Garcia ; Debora Gil; Francesc Carreras
A massively parallel computational electrophysiology model of the heart
2011
International Journal for Numerical Methods in Biomedical Engineering
27
1911-1929
Debora Gil; F. Javier Sanchez; Gloria Fernandez Esparrach; Jorge Bernal
3D Stable Spatio-temporal Polyp Localization in Colonoscopy Videos
2015
Computer-Assisted and Robotic Endoscopy. Revised selected papers of Second International Workshop, CARE 2015, Held in Conjunction with MICCAI 2015
9515
140-152
Aura Hernandez-Sabate; Jose Elias Yauri; Pau Folch; Miquel Angel Piera; Debora Gil
Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals
2022
Applied Sciences
12
2298
Jose Elias Yauri; Aura Hernandez-Sabate; Pau Folch; Debora Gil
Mental Workload Detection Based on EEG Analysis
2021
Artificial Intelligent Research and Development. Proceedings 23rd International Conference of the Catalan Association for Artificial Intelligence.
339
268-277
Aura Hernandez-Sabate; Debora Gil; Petia Radeva
On the usefulness of supervised learning for vessel border detection in IntraVascular Imaging
2005
Proceeding of the 2005 conference on Artificial Intelligence Research and Development
67-74
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