output
20202025
most citedAutomatic Assessment of Alzheimer's Disease Diagnosis Based on Deep Learning Techniques

169 citations

20 papers

cs.CV2025

Unsupervised training of keypoint-agnostic descriptors for flexible retinal image registration

David Rivas-Villar, Álvaro S. Hervella, José Rouco +1

Current color fundus image registration approaches are limited, among other things, by the lack of labeled data, which is even more significant in the medical domain, motivating th…

cs.CV2025

Unsupervised Deep Learning-based Keypoint Localization Estimating Descriptor Matching Performance

David Rivas-Villar, Álvaro S. Hervella, José Rouco +1

Retinal image registration, particularly for color fundus images, is a challenging yet essential task with diverse clinical applications. Existing registration methods for color fu…

cs.CR2024★ 3 cited

EAP-FIDO: A Novel EAP Method for Using FIDO2 Credentials for Network Authentication

Martiño Rivera-Dourado, Christos Xenakis, Alejandro Pazos +1

The adoption of FIDO2 authentication by major tech companies in web applications has grown significantly in recent years. However, we argue FIDO2 has broader potential applications…

cs.CR2024★ 7 cited

A Novel Protocol Using Captive Portals for FIDO2 Network Authentication

Martiño Rivera-Dourado, Marcos Gestal, Alejandro Pazos +1

FIDO2 authentication is starting to be applied in numerous web authentication services, aiming to replace passwords and their known vulnerabilities. However, this new authenticatio…

cs.AI2024★ 6 cited

Machine Learning in management of precautionary closures caused by lipophilic biotoxins

Andres Molares-Ulloa, Enrique Fernandez-Blanco, Alejandro Pazos +1

Mussel farming is one of the most important aquaculture industries. The main risk to mussel farming is harmful algal blooms (HABs), which pose a risk to human consumption. In Galic…

cs.LG2024★ 135 cited

Random Forest-Based Prediction of Stroke Outcome

Carlos Fernandez-Lozano, Pablo Hervella, Virginia Mato-Abad +9

We research into the clinical, biochemical and neuroimaging factors associated with the outcome of stroke patients to generate a predictive model using machine learning techniques…