Publications dans lesquelles il/elle collabore avec JUAN ELOY ARCO MARTÍN (27)

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

  4. Extracting Heart Rate Variability from NIRS Signals for an Explainable Detection of Learning Disorders

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

2023

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

    Pharmacological Research, Vol. 197

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

    Information Fusion, Vol. 100

  3. Enhancing Multimodal Patterns in Neuroimaging by Siamese Neural Networks with Self-Attention Mechanism

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

  4. Ensembling shallow siamese architectures to assess functional asymmetry in Alzheimer's disease progression

    Applied Soft Computing, Vol. 134

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

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

  6. Probabilistic Combination of Non-Linear Eigenprojections For Ensemble Classification

    IEEE Transactions on Emerging Topics in Computational Intelligence, Vol. 7, Núm. 5, pp. 1431-1441

  7. 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. 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)

  2. 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)

  3. 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)

  4. 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

  5. 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)

  6. Quantifying Inter-hemispheric Differences in Parkinson’s Disease Using Siamese Networks

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

  7. Tiled Sparse Coding in Eigenspaces for Image Classification

    International Journal of Neural Systems, Vol. 32, Núm. 3

  8. Towards Mixed Mode Biomarkers: Combining Structural and Functional Information by Deep Learning

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

  9. Uncertainty-driven ensembles of multi-scale deep architectures for image classification

    Information Fusion, Vol. 89, pp. 53-65