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ORCIDJuan A. Olmos
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2020 – today
- 2024
[j4]Edward Sandoval, Juan A. Olmos, Fabio Martínez
:
A self-supervised deep Riemannian representation to classify parkinsonian fixational patterns. Artif. Intell. Medicine 157: 102987 (2024)- [j3]Juan A. Olmos, Antoine Manzanera, Fabio Martínez:
Riemannian SPD learning to represent and characterize fixational oculomotor Parkinsonian abnormalities. Pattern Recognit. Lett. 177: 157-163 (2024) - 2023
- [j2]Santiago Niño, Juan A. Olmos, Juan C. Galvis, Fabio Martínez:
Parkinsonian gait patterns quantification from principal geodesic analysis. Pattern Anal. Appl. 26(2): 679-689 (2023) - [c5]Yesid Gutiérrez, Juan A. Olmos, Luis Carlos Guayacán, Fabio Martínez:
A Multimodal Geometric Deep Representation to Support Bi-Parametric Prostate Cancer Lesion Classification. ISBI 2023: 1-4 - [c4]Alejandra Moreno, Juan A. Olmos, Luis Carlos Guayacán, Fabio Martínez:
Exploiting Multi-Head Attention Maps Into A Deep Riemannian Representation to Quantify Pulmonary Nodules. ISBI 2023: 1-4 - 2022
- [j1]Oscar Mendoza, Fabio Martínez, Juan A. Olmos:
A local volumetric covariance descriptor for markerless Parkinsonian gait pattern quantification. Multim. Tools Appl. 81(21): 30733-30748 (2022) - [c3]Juan A. Olmos, Fabio Martínez:
A Riemannian Deep Learning Representation to Describe Gait Parkinsonian Locomotor Patterns. EMBC 2022: 3538-3541 - [c2]Juan A. Olmos, Juan C. Galvis, Fabio Martínez:
Gait Patterns Coded as Riemannian Mean Covariances to Support Parkinson's Disease Diagnosis. IBERAMIA 2022: 3-14 - [c1]Juan A. Olmos, Antoine Manzanera, Fabio Martínez:
An Oculomotor Digital Parkinson Biomarker from a Deep Riemannian Representation. ICPRAI (1) 2022: 677-687
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