Personal profile
ShortBio
Industrial electronics and automation engineer (2020) with a master’s degree in control, automation, and robotics (2022), both from UPV/EHU. After completing her master’s thesis in academic cooperation in the Computer Vision department of Tecnalia, she has been working at this technology centre since July 2022. She is currently a researcher in the Cores Visual area of the Digital operational unit and has collaborated on image processing projects for sectors such as steel industries, recycling/waste management and food industries, among others. In addition, she is currently dedicated to obtaining synthetic images using generative models, specifically Diffusion Models.
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
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SDG 9 Industry, Innovation, and Infrastructure -
SDG 12 Responsible Consumption and Production
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Collaborations and top research areas from the last five years
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On the analysis of adapting deep learning methods to hyperspectral imaging. Use case for WEEE recycling and dataset
Picon, A., Galan, P., Bereciartua-Perez, A. & Benito-del-Valle, L., 5 Apr 2025, In: Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy. 330, 125665.Research output: Contribution to journal › Article › peer-review
Open AccessFile3 Citations (Scopus)2 Downloads (Pure) -
Unleashing the Potential of Synthetic Images: A Study on Histopathology Image Classification
Benito-Del-Valle, L., Alvarez-Gila, A., Eguskiza, I. & Saratxaga, C. L., 2025, Computer Vision – ECCV 2024 Workshops, Proceedings. Del Bue, A., Canton, C., Pont-Tuset, J. & Tommasi, T. (eds.). Springer Science and Business Media Deutschland GmbH, p. 139-155 17 p. (Lecture Notes in Computer Science; vol. 15638 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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When synthetic plants get sick: Disease graded image datasets by novel regression-conditional diffusion models
Egusquiza, I., Benito-Del-Valle, L., Picón, A., Bereciartua-Pérez, A., Gómez-Zamanillo, L., Elola, A., Aramendi, E., Espejo, R., Eggers, T., Klukas, C. & Navarra-Mestre, R., Feb 2025, In: Computers and Electronics in Agriculture. 229, 109690.Research output: Contribution to journal › Article › peer-review
Open AccessFile7 Citations (Scopus)4 Downloads (Pure)
Datasets
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Unleashing the Potential of Synthetic Images: Histopathology Image Classification
Benito-Del-Valle, L. (Creator), Alvarez-Gila, A. (Creator), Egusquiza, I. (Creator) & L. Saratxaga, C. (Creator), Zenodo, 2025
Dataset
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Unleashing the Potential of Synthetic Images: Histopathology Image Classification
Benito-Del-Valle, L. (Creator), Alvarez-Gila, A. (Creator), Egusquiza, I. (Creator) & L. Saratxaga, C. (Creator), Zenodo, 8 Jan 2025
DOI: 10.5281/zenodo.13928371, https://zenodo.org/records/13928371
Dataset