3 papers
Interpretable Generative and Discriminative Learning for Multimodal and Incomplete Clinical Data
Albert Belenguer-Llorens, Carlos Sevilla-Salcedo, Janaina Mourao-Miranda +1
Real-world clinical problems are often characterized by multimodal data, usually associated with incomplete views and limited sample sizes in their cohorts, posing significant limi…
Unified Bayesian representation for high-dimensional multi-modal biomedical data for small-sample classification
Albert Belenguer-Llorens, Carlos Sevilla-Salcedo, Jussi Tohka +1
We present BALDUR, a novel Bayesian algorithm designed to deal with multi-modal datasets and small sample sizes in high-dimensional settings while providing explainable solutions.…
The Relevance Feature and Vector Machine for health applications
Albert Belenguer-Llorens, Carlos Sevilla-Salcedo, Emilio Parrado-Hernández +1
This paper presents the Relevance Feature and Vector Machine (RFVM), a novel model that addresses the challenges of the fat-data problem when dealing with clinical prospective stud…