A Robust and Fast Deep Learning-Based Method for Defect Classification in Steel Surfaces. - Fátima A. Saiz; Ismael Serrano; Iñigo Barandiaran; Jairo R. Sánchez (IEEE Conf. on Intelligent Systems, 2018)

COVID-19 Detection in Chest X-ray Images using a Deep Learning Approach. - Fátima A. Saiz; Iñigo Barandiaran (Int. J. Interact. Multim. Artif. Intell., 2020)

Synthetic Data Set Generation for the Evaluation of Image Acquisition Strategies Applied to Deep Learning Based Industrial Component Inspection Systems. - Fátima A. Saiz; Garazi Alfaro; Iñigo Barandiaran; Sara García; María del Puy Carretero; Manuel Graña (CEIG, 2021)

An Inspection and Classification System for Automotive Component Remanufacturing Industry Based on Ensemble Learning. - Fátima A. Saiz; Garazi Alfaro; Iñigo Barandiaran (Inf., 2021)

A Fast Deep Learning Based Approach for Unsupervised Anomaly Detection in 3D Data. - Sergio Presa; Fátima A. Saiz; Iñigo Barandiaran (ICFSP, 2022)

Photometric Stereo-Based Defect Detection System for Steel Components Manufacturing Using a Deep Segmentation Network. - Fátima A. Saiz; Iñigo Barandiaran; Ander Arbelaiz; Manuel Graña (Sensors, 2022)

Latent Diffusion Models to Enhance the Performance of Visual Defect Segmentation Networks in Steel Surface Inspection. - Jon Leiñena; Fátima A. Saiz; Iñigo Barandiaran (Sensors, 2024)

Handling Drift in Industrial Defect Detection Through MMD-Based Domain Adaptation. - Xuban Barberena; Fátima A. Saiz; Iñigo Barandiaran (VISIGRAPP (2): VISAPP, 2025)