6 papers
An explainable framework for the relationship between dementia and glucose metabolism patterns
C. Vázquez-GarcÃa, F. J. MartÃnez-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-GarcÃa, F. J. MartÃnez-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…
Is K-fold cross validation the best model selection method for Machine Learning?
Juan M Gorriz, R. Martin Clemente, F Segovia +3
As a technique that can compactly represent complex patterns, machine learning has significant potential for predictive inference. K-fold cross-validation (CV) is the most common a…
RESISTO Project: Automatic detection of operation temperature anomalies for power electric transformers using thermal imaging
David López-GarcÃa, FermÃn Segovia, Jacob RodrÃguez-Rivero +4
The RESISTO project represents a pioneering initiative in Europe aimed at enhancing the resilience of the power grid through the integration of advanced technologies. This includes…
RESISTO Project: Safeguarding the Power Grid from Meteorological Phenomena
Jacob RodrÃguez-Rivero, David López-GarcÃa, FermÃn Segovia +13
The RESISTO project, a pioneer innovation initiative in Europe, endeavors to enhance the resilience of electrical networks against extreme weather events and associated risks. Emph…