Juan A. Olmos

According to our database, Juan A. Olmos authored at least 8 papers between 2022 and 2024.

Collaborative distances:

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

2024

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

Juan A. Olmos, Antoine Manzanera, Fabio Martínez

Pattern Recognit. Lett., January, 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 Carlos 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 Carlos 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