Juan A. Olmos

According to our database, Juan A. Olmos authored at least 17 papers between 2022 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2026

A Geometric Multimodal Foundation Model Integrating BP-MRI and Clinical Reports in Prostate Cancer Classification.

Juan A. Olmos, Antoine Manzanera, Fabio Martínez

Proceedings of the 23rd IEEE International Symposium on Biomedical Imaging, 2026

2025

Learning a geometric deep representation to classify Parkinson smooth pursuit patterns.

Luis Fernando Celis, Juan A. Olmos, Antoine Manzanera, Fabio Martínez

Pattern Anal. Appl., September, 2025

Geometric multimodal learning to support prostate cancer diagnosis on limited and multicentric bi-parametric MRI data.

Juan A. Olmos, Antoine Manzanera, Fabio Martínez

Neural Comput. Appl., September, 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., June, 2025

A multimodal gait and ocular geometric representation to generate a Parkinson progression report.

John Archila, Ivan Peña, Luis Fernando Celis, Juan A. Olmos, Antoine Manzanera, Fabio Martínez

Eng. Appl. Artif. Intell., 2025

RIEMAE: Riemannian Masked Autoencoder for Classifying Malignant Prostate Cancer Patterns.

Edward Sandoval, Juan A. Olmos, Fabio Martínez

Proceedings of the 22nd IEEE International Symposium on Biomedical Imaging, 2025

A Second-Order Attention Mechanism for Prostate Cancer Segmentation and Detection in Bi-parametric MRI.

Mateo Ortiz, Juan A. Olmos, Fabio Martínez

Proceedings of the Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 2025

2024

Riemannian SPD learning to represent and characterize fixational oculomotor Parkinsonian abnormalities.

Juan A. Olmos, Antoine Manzanera, Fabio Martínez

Pattern Recognit. Lett., January, 2024

A self-supervised deep Riemannian representation to classify parkinsonian fixational patterns.

Edward Sandoval, Juan A. Olmos, Fabio Martínez

Artif. Intell. Medicine, 2024

A Geometric Attention Mechanism to Classify Parkinsonism Smooth Pursuit Patterns.

Luis Fernando Celis, Juan A. Olmos, Fabio Martínez

Proceedings of the Advances in Artificial Intelligence - IBERAMIA 2024, 2024

2023

Parkinsonian gait patterns quantification from principal geodesic analysis.

Santiago Niño, Juan A. Olmos, Juan C. Galvis, Fabio Martínez

Pattern Anal. Appl., May, 2023

Exploiting Multi-Head Attention Maps Into A Deep Riemannian Representation to Quantify Pulmonary Nodules.

Alejandra Moreno, Juan A. Olmos, Luis Guayacán, Fabio Martínez

Proceedings of the 20th IEEE International Symposium on Biomedical Imaging, 2023

A Multimodal Geometric Deep Representation to Support Bi-Parametric Prostate Cancer Lesion Classification.

Yesid Gutiérrez, Juan A. Olmos, Luis Guayacán, Fabio Martínez

Proceedings of the 20th IEEE International Symposium on Biomedical Imaging, 2023

2022

A local volumetric covariance descriptor for markerless Parkinsonian gait pattern quantification.

Oscar Mendoza, Fabio Martínez, Juan A. Olmos

Multim. Tools Appl., 2022

An Oculomotor Digital Parkinson Biomarker from a Deep Riemannian Representation.

Juan A. Olmos, Antoine Manzanera, Fabio Martínez

Proceedings of the Pattern Recognition and Artificial Intelligence, 2022

Gait Patterns Coded as Riemannian Mean Covariances to Support Parkinson's Disease Diagnosis.

Juan A. Olmos, Juan C. Galvis, Fabio Martínez

Proceedings of the Advances in Artificial Intelligence - IBERAMIA 2022, 2022

A Riemannian Deep Learning Representation to Describe Gait Parkinsonian Locomotor Patterns.

Juan A. Olmos, Fabio Martínez

Proceedings of the 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2022