Publikationen in Zusammenarbeit mit Forschern von University of Cambridge (18)

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

2022

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

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

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. Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications

    Neurocomputing, Vol. 410, pp. 237-270

  3. Autosomal dominantly inherited alzheimer disease: Analysis of genetic subgroups by machine learning

    Information Fusion, Vol. 58, pp. 153-167

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

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

2019

  1. A Machine Learning Approach to Reveal the NeuroPhenotypes of Autisms

    International Journal of Neural Systems, Vol. 29, Núm. 7

2018

  1. Assessing mild cognitive impairment progression using a spherical brain mapping of magnetic resonance imaging

    Journal of Alzheimer's Disease, Vol. 65, Núm. 3, pp. 713-729

  2. Convolutional Neural Networks for Neuroimaging in Parkinson's Disease: Is Preprocessing Needed?

    International Journal of Neural Systems, Vol. 28, Núm. 10

  3. Discriminative sparse features for alzheimer’s disease diagnosis using multimodal image data

    Current Alzheimer Research, Vol. 15, Núm. 1, pp. 67-79

  4. Ensemble of random forests One vs. Rest classifiers for MCI and AD prediction using ANOVA cortical and subcortical feature selection and partial least squares

    Journal of Neuroscience Methods, Vol. 302, pp. 47-57

2017

  1. A heavy tailed expectation maximization hidden markov random field model with applications to segmentation of MRI

    Frontiers in Neuroinformatics, Vol. 11

  2. A semi-supervised learning approach for model selection based on class-hypothesis testing

    Expert Systems with Applications, Vol. 90, pp. 40-49

  3. Case-Based Statistical Learning: A Non-Parametric Implementation with a Conditional-Error Rate SVM

    IEEE Access, Vol. 5, pp. 11468-11478

  4. On the brain structure heterogeneity of autism: Parsing out acquisition site effects with significance-weighted principal component analysis

    Human Brain Mapping, Vol. 38, Núm. 3, pp. 1208-1223

  5. Preprocessing of 18F-DMFP-PET data based on Hidden Markov random fields and the Gaussian distribution

    Frontiers in Aging Neuroscience, Vol. 9, Núm. OCT