Dr. Gilberto Ochoa-Ruiz

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Selected publications

• Daniel Flores-Araiza, Francisco Lopez-Tiro, Clément Larose, Salvador Hinojosa, Andres Mendez-Vazquez, Miguel Gonzalez-Mendoza, Gilberto Ochoa-Ruiz, Christian Daul, Improving prototypical parts abstraction for case-based reasoning explanations designed for the kidney stone type recognition, Artificial Intelligence in Medicine, Volume 170, 2025, 103266, ISSN 0933-3657

• M. A. Teevno, R. Martinez-Garcia-Peña, G. Ochoa-Ruiz and S. Ali, “Domain Generalization for Endoscopic Image Segmentation by Disentangling Style-Content Information and SuperPixel Consistency,” 2024 IEEE 37th International Symposium on Computer-Based Medical Systems (CBMS), Guadalajara, Mexico, 2024, pp. 383-390, doi: 10.1109/CBMS61543.2024.00070.

• Teevno, M.A., Ochoa-Ruiz, G., Ali, S. (2025). Tackling Domain Generalization for Out-of-Distribution Endoscopic Imaging. In: Xu, X., Cui, Z., Rekik, I., Ouyang, X., Sun, K. (eds) Machine Learning in Medical Imaging. MLMI 2024. Lecture Notes in Computer Science, vol 15242. Springer, Cham. https://doi.org/10.1007/978-3-031-73290-4_5

• Lopez-Tiro, Francisco, et al. (2024), On The In Vivo Recognition Of Kidney Stones Using Machine Learning. IEEE Access (2024), doi: 10.1109/ACCESS.2024.3351178. Gonzalez-Perez, R. et al. (2024) Generation of Realistic Images of Kidney Stones Using Diffusion Models, Symp. on Computer Based Medical Systems (CMBS), Guadalajara, Mexico, 10.1109/CBMS61543.2024.00029

• González-Zapata, J, Lopez-Tiro, Fr. et al. (2024). A metric learning approach for endoscopic kidney stone identification, Expert Systems with Applications, Elsevier https://doi.org/10.1016/j.eswa.2024.124711

• Reyes-Amezcua et al (2024a), Leveraging Pre-trained Models for Robust Federated Learning for Kidney Stone Type Recognition, 23rd Mex. International Conference on Artificial Intelligence (MICAI) Advances on Soft Computing (Springer), Puebla, Mexico https://doi.org/10.1007/978-3-031-75543-9_13

• Reyes-Amezcua et al (2024b), EndoDepth: A Benchmark for Assessing Robustness in Endoscopic Depth Prediction, 2nd Workshop on Data Eng. For Medical Imaging, collocated with MICCAI, Marrakech, Morocco https://doi.org/10.1007/978-3-031-73748-0_9

• Reyes-Amezcua et al (2024b), EndoDepth: A Benchmark for Assessing Robustness in Endoscopic Depth Prediction, 2nd Workshop on Data Eng. For Medical Imaging, collocated with MICCAI, Marrakech, Morocco

• Lopez-Tiro, F. et al. (2024). Boosting Kidney Stone Identification in Endoscopic Images Using Two-Step Transfer Learning. In: Calvo, H., Martínez-Villaseñor, L., Ponce, H. (eds) Advances in Soft Computing. MICAI 2023. Lecture Notes in Computer Science(), vol 14392. Springer, Cham. https://doi.org/10.1007/978-3-031-47640-2_11

• Daniel Flores-Araiza, Francisco Lopez-Tiro, Jonathan El-Beze, Jacques Hubert, Miguel Gonzalez-Mendoza, Gilberto Ochoa-Ruiz, Christian Daul; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2023, pp. 295-304

• D. Flores-Araiza et al., “On the Link Between Model Performance and Causal Scoring of Medical Image Explanations,” 2024 IEEE 37th International Symposium on Computer-Based Medical Systems (CBMS), Guadalajara, Mexico, 2024, pp. 1-8, doi: 10.1109/CBMS61543.2024.00009.

• Ali, M., Toman, R., Ochoa-Ruiz, G., Ali, S. (2026). PolypDINO: Adapting DINOv2 for Domain Generalized Polyp Segmentation. In: Ali, S., Hogg, D.C., Peckham, M. (eds) Medical Image Understanding and Analysis. MIUA 2025. Lecture Notes in Computer Science, vol 15918. Springer, Cham. https://doi.org/10.1007/978-3-031-98694-9_14

• Teevno, M. A., Ochoa-Ruiz, G., & Ali, S. (2022). A semi-supervised Teacher-Student framework for surgical tool detection and localization. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 11(4), 1033–1041. https://doi.org/10.1080/21681163.2022.2150688

• J.C. Angeles-Ceron, G. Ochoa-Ruiz, L. Chang, S. Ali, “Real-time Instance Segmentation of Surgical Instruments using Attention and Multi-scale Feature Fusion”, Under review at Medical Image Analysis, 2021

• G. Ochoa-Ruiz, F. Lopez-Tiro, D. Flores-Araiza, J. Elbeze, D.-H. Trinh, M. Gonzalez-Mendoza, P. Eschwege, V. Estrade, J. Hubert, C. Daul, “On the in vivo recognition of kidney stones using machine learning”, Computer Methods and Programs in Biomedicine (CMPB), Elsevier

• J. Rodriguez, G. Ochoa-Ruiz, C. Mata, “A Prostate MRI segmentation tool based on Active Contour Models using a GVF model”, Applied Sciences, Medical Informatics and Data Analysis, Vol. 10, Issue 18 Year: 2021

• C. Perez-Guerrero, A. Palacios, G. Ochoa-Ruiz, C. Mata, J. Cassal, M. Gonzalez-Mendoza, L.E. Falcon Morales, “Assessing the Applicability of Deep Learning Methods for Segmenting Geometrical Features in Large-Scale Jet Flames from Infrared Images”, Heliyon, Elsevier, 2021

• S. Basar, M. Ali, G. Ochoa-Ruiz, A. Waheed, M. Zareei, A. Adnan, “Unsupervised color image segmentation: A case of RGB histogram-based K-means clustering initialization”, Plos One, Vol. 15 Issue 10, Year: 2020 J. El Beze, C. Mazeaud, C. Daul, G. Ochoa-Ruiz, M. Daudon, P. Eschwège, J. Hubert, “Evaluation of automatic recognition of urolithiasis”, British Journal of Urology International (BJUI), 2021

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