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Parkinsonian gait patterns quantification from principal geodesic analysis.

, , , and . Pattern Anal. Appl., 26 (2): 679-689 (May 2023)

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Gait Patterns Coded as Riemannian Mean Covariances to Support Parkinson's Disease Diagnosis., , and . IBERAMIA, volume 13788 of Lecture Notes in Computer Science, page 3-14. Springer, (2022)Riemannian SPD learning to represent and characterize fixational oculomotor Parkinsonian abnormalities., , and . Pattern Recognit. Lett., (January 2024)A Riemannian Deep Learning Representation to Describe Gait Parkinsonian Locomotor Patterns., and . EMBC, page 3538-3541. IEEE, (2022)A Multimodal Geometric Deep Representation to Support Bi-Parametric Prostate Cancer Lesion Classification., , , and . ISBI, page 1-4. IEEE, (2023)Exploiting Multi-Head Attention Maps Into A Deep Riemannian Representation to Quantify Pulmonary Nodules., , , and . ISBI, page 1-4. IEEE, (2023)A local volumetric covariance descriptor for markerless Parkinsonian gait pattern quantification., , and . Multim. Tools Appl., 81 (21): 30733-30748 (2022)Parkinsonian gait patterns quantification from principal geodesic analysis., , , and . Pattern Anal. Appl., 26 (2): 679-689 (May 2023)An Oculomotor Digital Parkinson Biomarker from a Deep Riemannian Representation., , and . ICPRAI (1), volume 13363 of Lecture Notes in Computer Science, page 677-687. Springer, (2022)