Diego Castillo-Barnes
Orcid: 0000-0003-1635-5685
According to our database1,
Diego Castillo-Barnes
authored at least 35 papers
between 2017 and 2023.
Collaborative distances:
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Bibliography
2023
Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends.
Inf. Fusion, December, 2023
Nonlinear Weighting Ensemble Learning Model to Diagnose Parkinson's Disease Using Multimodal Data.
Int. J. Neural Syst., August, 2023
Using Explainable Artificial Intelligence in the Clock Drawing Test to Reveal the Cognitive Impairment Pattern.
Int. J. Neural Syst., April, 2023
Ensembling shallow siamese architectures to assess functional asymmetry in Alzheimer's disease progression.
Appl. Soft Comput., February, 2023
Revealing Patterns of Symptomatology in Parkinson's Disease: A Latent Space Analysis with 3D Convolutional Autoencoders.
CoRR, 2023
2022
Quantifying Differences Between Affine and Nonlinear Spatial Normalization of FP-CIT Spect Images.
Int. J. Neural Syst., 2022
Modelling the Progression of the Symptoms of Parkinsons Disease Using a Nonlinear Decomposition of 123I FP-CIT SPECT Images.
Proceedings of the Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications, 2022
Proceedings of the Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications, 2022
Automatic Classification System for Diagnosis of Cognitive Impairment Based on the Clock-Drawing Test.
Proceedings of the Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications, 2022
Evaluating Intensity Concentrations During the Spatial Normalization of Functional Images for Parkinson's Disease.
Proceedings of the Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications, 2022
CAD System for Parkinson's Disease with Penalization of Non-significant or High-Variability Input Data Sources.
Proceedings of the Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications, 2022
Quantifying Inter-hemispheric Differences in Parkinson's Disease Using Siamese Networks.
Proceedings of the Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications, 2022
2021
Statistical Agnostic Mapping: A framework in neuroimaging based on concentration inequalities.
Inf. Fusion, 2021
2020
Studying the Manifold Structure of Alzheimer's Disease: A Deep Learning Approach Using Convolutional Autoencoders.
IEEE J. Biomed. Health Informatics, 2020
Granger causality-based information fusion applied to electrical measurements from power transformers.
Inf. Fusion, 2020
Autosomal dominantly inherited alzheimer disease: Analysis of genetic subgroups by machine learning.
Inf. Fusion, 2020
Multivariate analysis of dual-point amyloid PET intended to assist the diagnosis of Alzheimer's disease.
Neurocomputing, 2020
Neurocomputing, 2020
Morphological Characterization of Functional Brain Imaging by Isosurface Analysis in Parkinson's Disease.
Int. J. Neural Syst., 2020
Optimized One vs One Approach in Multiclass Classification for Early Alzheimer's Disease and Mild Cognitive Impairment Diagnosis.
IEEE Access, 2020
2019
Int. J. Neural Syst., 2019
Proceedings of the Understanding the Brain Function and Emotions, 2019
Proceedings of the Understanding the Brain Function and Emotions, 2019
Comparison Between Affine and Non-affine Transformations Applied to I ^[123] [ 123 ] -FP-CIT SPECT Images Used for Parkinson's Disease Diagnosis.
Proceedings of the Understanding the Brain Function and Emotions, 2019
2018
Robust Ensemble Classification Methodology for I123-Ioflupane SPECT Images and Multiple Heterogeneous Biomarkers in the Diagnosis of Parkinson's Disease.
Frontiers Neuroinformatics, 2018
Deep Convolutional Autoencoders vs PCA in a Highly-Unbalanced Parkinson's Disease Dataset: A DaTSCAN Study.
Proceedings of the International Joint Conference SOCO'18-CISIS'18-ICEUTE'18, 2018
Classification Improvement for Parkinson's Disease Diagnosis Using the Gradient Magnitude in DaTSCAN SPECT Images.
Proceedings of the International Joint Conference SOCO'18-CISIS'18-ICEUTE'18, 2018
Using Early Acquisitions of Amyloid-PET as a Surrogate of FDG-PET: A Machine Learning Based Approach.
Proceedings of the 2018 International Workshop on Pattern Recognition in Neuroimaging, 2018
2017
Functional Brain Imaging Synthesis Based on Image Decomposition and Kernel Modeling: Application to Neurodegenerative Diseases.
Frontiers Neuroinformatics, 2017
A Heavy Tailed Expectation Maximization Hidden Markov Random Field Model with Applications to Segmentation of MRI.
Frontiers Neuroinformatics, 2017
A semi-supervised learning approach for model selection based on class-hypothesis testing.
Expert Syst. Appl., 2017
Proceedings of the Medical Image Understanding and Analysis - 21st Annual Conference, 2017
Automatic Separation of Parkinsonian Patients and Control Subjects Based on the Striatal Morphology.
Proceedings of the Natural and Artificial Computation for Biomedicine and Neuroscience, 2017
Proceedings of the Natural and Artificial Computation for Biomedicine and Neuroscience, 2017
On a Heavy-Tailed Intensity Normalization of the Parkinson's Progression Markers Initiative Brain Database.
Proceedings of the Natural and Artificial Computation for Biomedicine and Neuroscience, 2017