Guayacán, L.C., Manzanera, A. & Martínez, F. Quantification of Parkinsonian Kinematic Patterns in Body-Segment Regions During Locomotion. Journal of Medical and Biological Engineering. 42, 204–215 (2022). https://doi.org/10.1007/s40846-022-00691-x

Moreno, A., Olmos, J., Guayacán, L., & Martínez, F. (2023, April). Exploiting Multi-Head Attention Maps Into A Deep Riemannian Representation to Quantify Pulmonary Nodules. In 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI) (pp. 1-4). IEEE.

Gutiérrez, Y., Olmos, J., Guayacán, L., & Martínez, F. (2023, April). A Multimodal Geometric Deep Representation to Support Bi-Parametric Prostate Cancer Lesion Classification. In 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI) (pp. 1-4). IEEE.

Niño, S., Olmos, J. A., Galvis, J. C., & Martínez, F. (2023). Parkinsonian gait patterns quantification from principal geodesic analysis. Pattern Analysis and Applications, 26(2), 679-689

Gómez, S., Florez, S., Mantilla, D., Camacho, P., Tarazona, N., & Martínez, F. (2023, April). An attentional unet with an auxiliary class learning to support acute ischemic stroke segmentation on CT. In Medical Imaging 2023: Image Processing (Vol. 12464, pp. 144-148). SPIE.

Gómez, S., Mantilla, D., Rangel, E., Ortiz, A., Vera, D. D., & Martínez, F. (2023). A deep supervised cross-attention strategy for ischemic stroke segmentation in MRI studies. Biomedical Physics & Engineering Express, 9(3), 035026.

Ruiz-García, L. M., Guayacán-Chaparro, L. C., & Martínez-Carrillo, F. (2023). Attention Maps to Highlight Potential Polyps during Colonoscopy. Tecnura, 27(75), 51-71.

Castro, S., Romo-Bucheli, D., Guayacán, L., & Martínez, F. (2023, April). Fast detection and localization of mitosis using a semi-supervised deep representation. In Medical Imaging 2023: Digital and Computational Pathology (Vol. 12471, pp. 443-446). SPIE.

Moreno, A., Rueda, A., & Martinez, F. (2023, April). A volumetric multi-head attention strategy for lung nodule classification in CT. In Medical Imaging 2023: Computer-Aided Diagnosis (Vol. 12465, pp. 627-630). SPIE.

Viáfara, C. C., Valenzuela, B., Martínez, F., & Penagos, J. J. (2023). A method to analyze wear mechanisms on worn chute lining surfaces using computer vision tools. Tribology International, 186, 108586.

Gómez, S., Mantilla, D., Garzón, G., Rangel, E., Ortiz, A., Sierra-Jerez, F., & Martínez, F. (2023). APIS: A paired CT-MRI dataset for ischemic stroke segmentation challenge. arXiv preprint arXiv:2309.15243.

Olmos, J., Manzanera, A., & Martínez, F. (2023). Riemannian SPD learning to represent and characterize fixational oculomotor Parkinsonian abnormalities. Pattern Recognition Letters.

Olmos, J., Valenzuela, B., & Martínez, F. (2023). Quantification of Parkinsonian unilateral involvement from ocular fixational patterns using a deep video representation. Health and Technology, 1-8.

Romero, W. D., Torres-Bermudez, S., Valenzuela, B., Viáfara, C. C., Meléndez, A. M., & Martínez, F. (2023). Geometrical recognition of metallic foam microstructures using a deep learning approach. Materials Today Communications, 107407.

Moreno, A., Rueda, A., & Martinez, F. (2022, March). A Multi-Scale Self-Attention Network to Discriminate Pulmonary Nodules. In 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) (pp. 1-4). IEEE.

Sierra-Jerez, F., & Martínez, F. (2022). A deep representation to fully characterize hyperplastic, adenoma, and serrated polyps on narrow band imaging sequences. Health and Technology, 1-13.

Plazas, M., Ramos, R., León, F., & Martínez, F. Towards reduction of expert bias on Gleason score classification via a semi-supervised deep learning strategy. In Proc. of SPIE Vol (Vol. 12032, pp. 120322O-1)

Moreno, A., Rodríguez, J., & Martínez, F. (2022). Kinematic motion representation in Cine-MRI to support cardiac disease classification. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1-12.

Mendoza, Oscar, Fabio Martínez, and Juan Olmos. "A local volumetric covariance descriptor for markerless Parkinsonian gait pattern quantification." Multimedia Tools and Applications (2022): 1-16.

Olmos, Juan, Antoine Manzanera, and Fabio Martínez. "An Oculomotor Digital Parkinson Biomarker from a Deep Riemannian Representation." International Conference on Pattern Recognition and Artificial Intelligence. 2022.

Juan Olmos, Juan Galvis and Fabio Martínez. "A geometric mean algorithm of symmetric positive definite matrices" Accepted in II Conferencia Colombiana de Matemáticas Aplicadas e Industriales (MAPI2). 2022.

William Omar Contreras López, Luis Guayacan Ing, Fabio Martinez Ing, Paula Alejandra Navarro, Melisa Ibarra Quiñonez, Erich Talamoni Fonoff. Abstract #19 Tremor Quantification Through Event-Based Movement Trajectory Modeling Before and After Unilateral Radiofrequency Sub-Thalamotomy in an 83-Years-Old Parkinson Patient: A Case Report. World Neurosurgery. Volume 158, 2022. Page 354. ISSN 1878-750. https://doi.org/10.1016/j.wneu.2021.10.051.

León, F., & Martínez, F. (2022). A multitask deep representation for Gleason score classification to support grade annotations. Biomedical Physics & Engineering Express, 8(3), 035021.

Garzón, G., Gomez, S., Mantilla, D., & Martínez, F. (2022, July). A deep CT to MRI unpaired translation that preserve ischemic stroke lesions. In 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 2708-2711). IEEE.

Olmos, Juan, and Fabio Martínez. "A Riemannian Deep Learning Representation to Describe Gait Parkinsonian Locomotor Patterns." 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2022.

Olmos, J., Galvis, J., & Martínez, F. (2022, November). Gait Patterns Coded as Riemannian Mean Covariances to Support Parkinson’s Disease Diagnosis. In Ibero-American Conference on Artificial Intelligence (pp. 3-14).

F. Sierra-Jerez, J. Ruiz and F. Martínez, "A Non-Aligned Deep Representation to Enhance Standard Colonoscopy Observations from Vascular Narrow Band Polyp Patterns," 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Glasgow, Scotland, United Kingdom, 2022, pp. 1671-1674, doi: 10.1109/EMBC48229.2022.9871752.

Rangel, E., & Martínez, F. (2022, July). A Parkinsonian Digital Biomarker Learned as an Anomaly Deep Generative Representation. In 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 4188-4191). IEEE.

L. Ruiz and F. Martínez, "Weakly Supervised Polyp Segmentation from an Attention Receptive Field Mechanism," 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Glasgow, Scotland, United Kingdom, 2022, pp. 3745-3748, doi: 10.1109/EMBC48229.2022.9871158.

B. Valenzuela, J. Arevalo, W. Contreras and F. Martinez, "A Spatio-Temporal Hypomimic Deep Descriptor to Discriminate Parkinsonian Patients," 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Glasgow, Scotland, United Kingdom, 2022, pp. 4192-4195, doi: 10.1109/EMBC48229.2022.9871753.

Gutiérrez, Y., Arevalo, J., & Martínez, F. (2022). An inception-based deep multiparametric net to classify clinical significance MRI regions of prostate cancer. Physics in Medicine & Biology, 67(22), 225004.

Rodríguez J. et al. (2021) Understanding Motion in Sign Language: A New Structured Translation Dataset. In: Ishikawa H., Liu CL., Pajdla T., Shi J. (eds) Computer Vision – ACCV 2020. ACCV 2020. Lecture Notes in Computer Science, vol 12627. Springer, Cham.

Garzon, G., Martinez, F. "Local Trajectory Occurrence Patterns for Partial Action and Gesture Recognition". International Journal on Advanced Science, Engineering and Information Technology. Vol 11. 2021.

Carrillo, F. M., Gouiffès, M., Villamizar, G. G., & Manzanera, A. (2021). A compact and recursive Riemannian motion descriptor for untrimmed activity recognition. Journal of Real-Time Image Processing, 1-14.

Ruano J, Arcila J, Romo Bucheli D, Vargas C, Rodriguez J, Mendoza O, Plazas M, Bautista L, Villamizar J, Pedraza G, Moreno A, Valenzuela D, Vazquez L, Valenzuela C, Camacho P, Mantilla D, Martínez Carrillo F. Representaciones basadas en aprendizaje profundo para dar soporte al diagnóstico del COVID-19 en cortes de TC. biomedica. Disponible en: https://revistabiomedica.org/index.php/biomedica/article/view/5927

Archila, J., Manzanera, A., & Martinez, F. (2021). A multimodal Parkinson quantification by fusing eye and gait motion patterns, using covariance descriptors, from non-invasive computer vision. Computer Methods and Programs in Biomedicine, 106607.

Archila, J., Manzanera, A., & Martínez, F. (2021, December). A recurrent approach for predicting Parkinson stage from multimodal videos. In 17th International Symposium on Medical Information Processing and Analysis (Vol. 12088, pp. 37-45). SPIE.

Rodríguez, J., Romo-Bucheli, D., Sierra, F., Valenzuela, D., Valenzuela, C., Vasquez, L., ... & Martínez, F. (2021, April). A Covid-19 Patient Severity Stratification using a 3D Convolutional Strategy on CT-Scans. In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) (pp. 1665-1668). IEEE.

Rodriguez, J., & Martínez, F. (2021). How important is motion in sign language translation?. IET Computer Vision, 15(3), 224-234.

Guayacán LC, Martínez F. Visualising and quantifying relevant parkinsonian gait patterns using 3D convolutional network. J Biomed Inform. 2021 Nov;123:103935. doi: 10.1016/j.jbi.2021.103935. Epub 2021 Oct 24. PMID: 34699990.

Pena, H., Gomez, S., Romo-Bucheli, D., & Martinez, F. (2021). Cardiac Disease Representation Conditioned by Spatio-temporal Priors in Cine-MRI Sequences Using Generative Embedding Vectors. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2021, 5570–5573. https://doi.org/10.1109/EMBC46164.2021.9630115

Gómez, S., Romo-Bucheli, D. & Martínez, F. A digital cardiac disease biomarker from a generative progressive cardiac cine-MRI representation. Biomed. Eng. Lett. (2021). https://doi.org/10.1007/s13534-021-00212-w

Gómez, A., León-Pérez, F., Plazas-Wadynski, M., & Martínez-Carrilo, F. (2021). Segmentación multinivel de patrones de Gleason usando representaciones convolucionales en imágenes histopatológicas. TecnoLógicas, 24(52), e2132-e2132.

León, F., & Martínez, F. (2021, November). Learning a Triplet Embedding Distance to Represent Gleason Patterns. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 3229-3232). IEEE.

Plazas, M., Ramos-Pollán, R., & Martínez, F. (2021). Ensemble-based approach for semisupervised learning in remote sensing. Journal of Applied Remote Sensing, 15(3), 034509.

Gómez, A., León-Pérez, F., Plazas-Wadynski, M., & Martínez-Carrillo, F. (2021). Multilevel Segmentation of Gleason Patterns using Convolutional Representations in Histopathological Images. TecnoLógicas, 24(52), 176-196.

Sierra, Franklin, Yesid Gutiérrez, and Fabio Martínez. "An online deep convolutional polyp lesion prediction over Narrow Band Imaging (NBI)." 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2020.

León, F., Plazas, M., & Martínez, F. (2020, January). An inception deep architecture to differentiate close-related Gleason prostate cancer scores. In 15th International Symposium on Medical Information Processing and Analysis (Vol. 11330, p. 113300D). International Society for Optics and Photonics.

Yesid Gutiérrez, John Arevalo, and Fabio Martínez "A Ktrans deep characterization to measure clinical significance regions on prostate cancer", Proc. SPIE 11330, 15th International Symposium on Medical Information Processing and Analysis, 113300C (3 January 2020)

Salazar, I., Pertuz, S., Contreras, W., & Martínez, F. (2020). A convolutional oculomotor representation to model parkinsonian fixational patterns from magnified videos. Pattern Analysis and Application

Guayacán, L. C., Rangel, E., & Martínez, F. (2020, July). Towards understanding spatio-temporal parkinsonian patterns from salient regions of a 3D convolutional network. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 3688-3691). IEEE.

Salazar, I., Pertuz, S., & Martínez, F. (2020). Multi-modal RGB-D Image Segmentation from Appearance and Geometric Depth Maps. TecnoLógicas, 23(48), 143-161.

Ruiz L, Guayacan L, Martínez F. (2019). Automatic polyp detection from a regional appearance model and a robust dense Hough coding. In 2019 XXII Symposium on Image, Signal Processing and Artificial Vision (STSIVA) (pp. 1-5). IEEE.

Moreno A., Rodríguez, J., Martínez F. (2019). Regional Multiscale Motion Representation for Cardiac Disease Prediction. In 2019 XXII Symposium on Image, Signal Processing and Artificial Vision (STSIVA) (pp. 1-5). IEEE.

Guayacán, L. C., Valenzuela, B., & Martinez, F. (2018, December). Parkinsonian gait characterization from regional kinematic trajectories. In 14th International Symposium on Medical Information Processing and Analysis (Vol. 10975, p. 1097502). International Society for Optics and Photonics.

Gutiérrez, Y., Garzón, G., & Martínez, F. (2019, April). Towards clinical significance prediction using k trans evidences in prostate cancer. In 2019 XXII Symposium on Image, Signal Processing and Artificial Vision (STSIVA) (pp. 1-5). IEEE.

Garzón, G., & Martínez, F. (2019). A Fast Action Recognition Strategy Based on Motion Trajectory Occurrences. Pattern Recognition and Image Analysis, 29(3), 447-456.

Garzón, G., & Martínez, F. (2019, March). Online Action Recognition from Trajectory Occurrence Binary Patterns (ToBPs). In The International Conference on Advances in Emerging Trends and Technologies (pp. 409-418). Springer, Cham.

Valenzuela, B., Viáfara, C., & Martínez, F. (2019, April). Analysis of worn surface images using gradient-based descriptors. In 2019 XXII Symposium on Image, Signal Processing and Artificial Vision (STSIVA) (pp. 1-5). IEEE.

Valenzuela, B., Salazar, I., & Martínez, F. (2019, April). Lagrangian center of mass (CoM t) magnification to stand out main parkinsonian gait events. In 2019 XXII Symposium on Image, Signal Processing and Artificial Vision (STSIVA) (pp. 1-5). IEEE.

F. Castillo, L. Bautista, Martínez F, “3d+t dense motion trajectories as kinematic primitives to recognize gestures on depth video sequences”, Revista Politécnica, vol. 15, no.29 pp.82-94, 2019. DOI: 10.33571/rpolitec.v15n29a7

C. Gonzalez, C.C. Viafara, J.J. Coronado, F. Martinez. (2019). Automatic recognition of worn surfaces exhibiting severe and mild abrasive wear regimes. Wear, 426, 1702-1711.

Salazar, I., Pertuz, S., Contreras, W., & Martínez, F. (2019, October). Parkinsonian ocular fixation patterns from magnified videos and CNN features. In Iberoamerican Congress on Pattern Recognition (pp. 740-750). Springer, Cham.

Moreno, W., Garzón, G., & Martínez, F. (2018, September). Frame-Level Covariance Descriptor for Action Recognition. In Colombian Conference on Computing (pp. 276-290). Springer, Cham.

Contreras, S., Salazar, I., & Martínez, F. (2018). Parkinsonian hand tremor characterization from magnified video sequences. Proceedings Volume 10975, 14th International Symposium on Medical Information Processing and Analysis. spiedigitallibrary.

Sarmiento, E., Pico, J., & Martinez, F. (2018, April). Cardiac disease prediction from spatio-temporal motion patterns in cine-mri. In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) (pp. 1305-1308). IEEE.

Rodríguez J., Martínez F. (2018) A Kinematic Gesture Representation Based on Shape Difference VLAD for Sign Language Recognition. In: Chmielewski L., Kozera R., Orłowski A., Wojciechowski K., Bruckstein A., Petkov N. (eds) Computer Vision and Graphics. ICCVG 2018. Lecture Notes in Computer Science, vol 11114. Springer, Cham.

Rodríguez J., Martínez F. (2018) Towards On-Line Sign Language Recognition Using Cumulative SD-VLAD Descriptors. In: Serrano C. J., Martínez-Santos J. (eds) Advances in Computing. CCC 2018. Communications in Computer and Information Science, vol 885. Springer, Cham.

Martínez, F., Manzanera, A., & Romero, E. (2017). Spatio‐temporal multi‐scale motion descriptor from a spatially‐constrained decomposition for online action recognition. IET Computer Vision, 11(7), 541-549.