A Riemannian Deep Learning Representation to Describe Gait Parkinsonian Locomotor Patterns. - Juan A. Olmos; Fabio Martínez (EMBC, 2022)

Gait Patterns Coded as Riemannian Mean Covariances to Support Parkinson's Disease Diagnosis. - Juan A. Olmos; Juan C. Galvis; Fabio Martínez (IBERAMIA, 2022)

An Oculomotor Digital Parkinson Biomarker from a Deep Riemannian Representation. - Juan A. Olmos; Antoine Manzanera; Fabio Martínez (ICPRAI (1), 2022)

A local volumetric covariance descriptor for markerless Parkinsonian gait pattern quantification. - Oscar Mendoza; Fabio Martínez; Juan A. Olmos (Multim. Tools Appl., 2022)

A Multimodal Geometric Deep Representation to Support Bi-Parametric Prostate Cancer Lesion Classification. - Yesid Gutiérrez; Juan A. Olmos; Luis Guayacán; Fabio Martínez (ISBI, 2023)

Exploiting Multi-Head Attention Maps Into A Deep Riemannian Representation to Quantify Pulmonary Nodules. - Alejandra Moreno; Juan A. Olmos; Luis Guayacán; Fabio Martínez (ISBI, 2023)

Parkinsonian gait patterns quantification from principal geodesic analysis. - Santiago Niño; Juan A. Olmos; Juan C. Galvis; Fabio Martínez (Pattern Anal. Appl., 2023)

A Geometric Attention Mechanism to Classify Parkinsonism Smooth Pursuit Patterns. - Luis Fernando Celis; Juan A. Olmos; Fabio Martínez (IBERAMIA, 2024)

A self-supervised deep Riemannian representation to classify parkinsonian fixational patterns. - Edward Sandoval; Juan A. Olmos; Fabio Martínez (Artif. Intell. Medicine, 2024)

Riemannian SPD learning to represent and characterize fixational oculomotor Parkinsonian abnormalities. - Juan A. Olmos; Antoine Manzanera; Fabio Martínez (Pattern Recognit. Lett., 2024)

RIEMAE: Riemannian Masked Autoencoder for Classifying Malignant Prostate Cancer Patterns. - Edward Sandoval; Juan A. Olmos; Fabio Martínez (ISBI, 2025)

Parkinson disease classification: a comparison of quantum and RBF kernels using support vector machine. - Gregoire Cattan; Juan A. Olmos; Yash Chauhan; Fabio Martínez (Quantum Mach. Intell., 2025)