Publicaciones en las que colabora con JAVIER RAMÍREZ PÉREZ DE INESTROSA (22)

2024

  1. A Comparative Study of Deep Learning Approaches for Cognitive Impairment Diagnosis Based on the Clock-Drawing Test

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

  2. A Cross-Modality Latent Representation for the Prediction of Clinical Symptomatology in Parkinson’s Disease

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

  3. Bridging Imaging and Clinical Scores in Parkinson's Progression via Multimodal Self-Supervised Deep Learning

    International Journal of Neural Systems, Vol. 34, Núm. 8

2023

  1. A non-parametric statistical inference framework for Deep Learning in current neuroimaging

    Information Fusion, Vol. 91, pp. 598-611

  2. Applications of machine learning and deep learning in SPECT and PET imaging: General overview, challenges and future prospects

    Pharmacological Research, Vol. 197

  3. Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends

    Information Fusion, Vol. 100

  4. Nonlinear Weighting Ensemble Learning Model to Diagnose Parkinson's Disease Using Multimodal Data

    International Journal of Neural Systems, Vol. 33, Núm. 8

  5. Using Explainable Artificial Intelligence in the Clock Drawing Test to Reveal the Cognitive Impairment Pattern

    International Journal of Neural Systems, Vol. 33, Núm. 4

2022

  1. A Connection between Pattern Classification by Machine Learning and Statistical Inference with the General Linear Model

    IEEE Journal of Biomedical and Health Informatics, Vol. 26, Núm. 11, pp. 5332-5343

  2. Analysis of the Neurological Hallmarks of COVID-19. A Multivariate Approach Using 18F-FDG-PET Data

    2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference

  3. Analyzing Statistical Inference Maps Using MRI Images for Parkinson’s Disease

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

  4. Automatic Classification System for Diagnosis of Cognitive Impairment Based on the Clock-Drawing Test

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

  5. CAD System for Parkinson’s Disease with Penalization of Non-significant or High-Variability Input Data Sources

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

  6. Ensemble Learning Using Weighted Convolutional Neural Networks for Parkinson's Disease

    2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference

  7. Evaluating Intensity Concentrations During the Spatial Normalization of Functional Images for Parkinson’s Disease

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

  8. Quantifying Differences between Affine and Nonlinear Spatial Normalization of FP-CIT Spect Images

    International Journal of Neural Systems, Vol. 32, Núm. 5

  9. Standardization of Agnostic Learning Techniques in Neuroimaging: a Case Study in EEG

    2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference

2021

  1. Statistical Agnostic Mapping: A framework in neuroimaging based on concentration inequalities

    Information Fusion, Vol. 66, pp. 198-212

2020

  1. Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation

    Information Fusion, Vol. 64, pp. 149-187

  2. Granger causality-based information fusion applied to electrical measurements from power transformers

    Information Fusion, Vol. 57, pp. 59-70