Emilio Delgado

Emilio Delgado

Researcher at the University of Extremadura, specializing in Machine Learning and Deep Learning for healthcare. His work focuses on applying advanced AI techniques to process and analyze large-scale clinical and medical imaging data, aiming to improve healthcare outcomes and quality of life. Passionate about optimizing deep learning algorithms for medical applications, Emilio is committed to innovation, accuracy, and data privacy in AI-driven healthcare solutions.

Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

Prompt Once, Segment Everything: Leveraging SAM 2 Potential for Infinite Medical Image Segmentation with a Single Prompt

Published in Algorithms, 2025

This paper introduces a novel approach leveraging SAM 2’s video segmentation capabilities to reduce the prompts required to segment an entire volume of medical images.

Recommended citation: Gutiérrez, J.D., Delgado, E., Breuer, C., Conejero, J.M., & Rodriguez-Echeverria, R. (2025). "Prompt Once, Segment Everything: Leveraging SAM 2 Potential for Infinite Medical Image Segmentation with a Single Prompt." Algorithms, 18(4), 227.
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Advancing precision in medical image segmentation: A performance analysis of loss functions for COVID-19 lung infection segmentation in computed tomography images

Published in IET Image Processing, 2024

This study evaluates the effectiveness of three loss functions—Asymmetric Unified Focal Loss (AUFL), Dice Similarity Coefficient Loss (DSCL), and Cross-Entropy (CE)—for segmenting COVID-19 lung infections in computed tomography images.

Recommended citation: Delgado, E., Rodriguez-Echeverria, R., Fernández-García, A.J., Gutiérrez, J.D., & Suero Rodrigo, M.Á. (2024). "Advancing precision in medical image segmentation: A performance analysis of loss functions for COVID-19 lung infection segmentation in computed tomography images." IET Image Processing.
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No More Training: SAM’s Zero-Shot Transfer Capabilities for Cost-Efficient Medical Image Segmentation

Published in IEEE Access, 2024

This paper showcases SAM’s robustness and exceptional performance in medical image segmentation, even in the absence of direct training on these image types.

Recommended citation: Gutiérrez, J.D., Rodriguez-Echeverria, R., Delgado, E., Suero Rodrigo, M.Á., & Sánchez-Figueroa, F. (2024). 'No More Training: SAM's Zero-Shot Transfer Capabilities for Cost-Efficient Medical Image Segmentation.' IEEE Access.
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