104 citations · 159 across the 8 of their papers we have counts for
11 papers
An explainable framework for the relationship between dementia and glucose metabolism patterns
C. Vázquez-García, F. J. Martínez-Murcia, F. Segovia Román +6
High-dimensional neuroimaging data presents challenges for assessing neurodegenerative diseases due to complex non-linear relationships. Variational Autoencoders (VAEs) can encode…
Uncovering Neuroimaging Biomarkers of Brain Tumor Surgery with AI-Driven Methods
Carmen Jimenez-Mesa, Yizhou Wan, Guilio Sansone +7
Brain tumor resection is a highly complex procedure with profound implications for survival and quality of life. Predicting patient outcomes is crucial to guide clinicians in balan…
Tutorial: VAE as an inference paradigm for neuroimaging
C. Vázquez-García, F. J. Martínez-Murcia, F. Segovia Román +1
In this tutorial, we explore Variational Autoencoders (VAEs), an essential framework for unsupervised learning, particularly suited for high-dimensional datasets such as neuroimagi…
Statistical Agnostic Regression: a machine learning method to validate regression models
Juan M Gorriz, J. Ramirez, F. Segovia +3
Regression analysis is a central topic in statistical modeling, aimed at estimating the relationships between a dependent variable, commonly referred to as the response variable, a…
EEG Connectivity Analysis Using Denoising Autoencoders for the Detection of Dyslexia
Francisco Jesus Martinez-Murcia, Andrés Ortiz, Juan Manuel Górriz +4
The Temporal Sampling Framework (TSF) theorizes that the characteristic phonological difficulties of dyslexia are caused by an atypical oscillatory sampling at one or more temporal…
Convolutional Neural Networks for Neuroimaging in Parkinson's Disease: Is Preprocessing Needed?
Francisco J. Martinez-Murcia, Juan M. Górriz, Javier Ramírez +1
Spatial and intensity normalization are nowadays a prerequisite for neuroimaging analysis. Influenced by voxel-wise and other univariate comparisons, where these corrections are ke…