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David Cárdenas-Peña
Researcher at Technological University of Pereira
Publications - 56
Citations - 686
David Cárdenas-Peña is an academic researcher from Technological University of Pereira. The author has contributed to research in topics: Image segmentation & Computer science. The author has an hindex of 8, co-authored 50 publications receiving 543 citations. Previous affiliations of David Cárdenas-Peña include National University of Colombia.
Papers
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Journal ArticleDOI
Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge
Esther E. Bron,Marion Smits,Wiesje M. van der Flier,Hugo Vrenken,Frederik Barkhof,Philip Scheltens,Janne M. Papma,Rebecca M. E. Steketee,Carolina Méndez Orellana,Rozanna Meijboom,Madalena Pinto,Joana R. Meireles,Carolina Garrett,António J. Bastos-Leite,Ahmed Abdulkadir,Olaf Ronneberger,Nicola Amoroso,Roberto Bellotti,David Cárdenas-Peña,Andrés Marino Álvarez-Meza,Chester V. Dolph,Khan M. Iftekharuddin,Simon Fristed Eskildsen,Pierrick Coupé,Vladimir S. Fonov,Katja Franke,Christian Gaser,Christian Ledig,Ricardo Guerrero,Tong Tong,Katherine R. Gray,Elaheh Moradi,Jussi Tohka,Alexandre Routier,Stanley Durrleman,Alessia Sarica,Giuseppe Di Fatta,Francesco Sensi,Andrea Chincarini,Garry Smith,Zhivko Stoyanov,Lauge Sørensen,Mads Nielsen,Sabina Tangaro,Paolo Inglese,Christian Wachinger,Martin Reuter,John C. van Swieten,Wiro J. Niessen,Stefan Klein +49 more
TL;DR: A grand challenge to objectively compare algorithms based on a clinically representative multi-center data set of three diagnostic groups, finding the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity.
Journal ArticleDOI
Evaluation of segmentation methods on head and neck CT: Auto-segmentation challenge 2015
Patrik Raudaschl,Paolo Zaffino,Gregory C. Sharp,Maria Francesca Spadea,Antong Chen,Benoit M. Dawant,Thomas Albrecht,Tobias Gass,Christoph Langguth,Marcel Lüthi,Florian Jung,Oliver Knapp,Stefan Wesarg,Richard Mannion-Haworth,Michael A. Bowes,Annaliese Ashman,Gwenael Guillard,Alan Brett,G.R. Vincent,Mauricio Orbes-Arteaga,David Cárdenas-Peña,Germán Castellanos-Domínguez,Nava Aghdasi,Yangming Li,Angelique M. Berens,Kris S. Moe,Blake Hannaford,Rainer Schubert,Karl D. Fritscher +28 more
TL;DR: The results demonstrate a clear tendency toward more general purpose and fewer structure‐specific segmentation algorithms in the state‐of‐the‐art in segmentation of organs at risk for radiotherapy treatment.
Book ChapterDOI
Unsupervised Kernel Function Building Using Maximization of Information Potential Variability
TL;DR: Results show that presented approach allows to compute RKHS’s favoring data groups separability, attaining suitable learning performances in comparison with state of the art algorithms.
Journal ArticleDOI
Selection of time-variant features for earthquake classification at the Nevado-del-Ruiz volcano
TL;DR: A feature selection strategy based on a relevance measure of time-variant features for seismic event classification and a simple k-nearest neighbor classification rule is used to properly determine the dimension of the final feature set.
Journal ArticleDOI
Centered Kernel Alignment Enhancing Neural Network Pretraining for MRI-Based Dementia Diagnosis.
TL;DR: This work introduces a novel supervised pretraining approach that takes advantage of the artificial neural network (ANN) for complex classification tasks and outperforms all the baselines at the time it reduces the class biasing.