Research lines
Over the years, my work in Artificial Intelligence has evolved across multiple fields, ranging from medical imaging and biomedical signal processing to cultural heritage, psychology, and audio enhancement, reflecting a broad interdisciplinary background. Today, my research is primarily focused on AI applications in medicine, with active projects in medical imaging, brain signal analysis, and trustworthy deep learning. These lines integrate both methodological innovation and real clinical impact, developed through collaborations with hospitals, research institutes, and international partners.
Publications
Nogales, A., Maitin, A. M., & García-Tejedor, Á. J. (2026). Best Practices to Train Accurate Deep Learning Models: A General Methodology. International Journal of Advanced Computer Science and Applications, 17(2).
Nogales, A., Garrido, M. C., Guitian, A., Rodriguez-Peralto, J. L., Villanueva, C. P., Díaz-Prieto, D., & García-Tejedor, Á. J. (2025). A Hybrid Artificial Intelligence Framework for Melanoma Diagnosis Using Histopathological Images. Technologies, 13(8), 330.
Carrilero-Mardones, M., Parras-Jurado, M., Nogales, A., Pérez-Martín, J., & Díez, F. J. (2024). Deep learning for describing breast ultrasound images with BI-RADS terms. Journal of Imaging Informatics in Medicine, 37(6), 2940-2954.