@inproceedings{platas-etal-2026-extracting,
title = "Extracting Medical Image-Related Entities from {S}panish Electronic Health Records Using {NER} Methods",
author = "Platas, Alexander and
Merino, Marcos and
Zotova, Elena and
Cuadros, Montse and
L{\'o}pez-Linares, Karen and
P{\'e}rez de Mendiola, Mikel and
G{\'a}lvez, Mar{\'i}a and
Barba, Cristina and
Asla, Ant{\'o}n",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.829/",
doi = "10.63317/4t6agzu5ygqr",
pages = "10569--10578",
abstract = "This paper presents a novel corpus in Spanish tailored for the extraction of medical image-related entities from radiological reports using Named Entity Recognition (NER) methods. The dataset was created by aggregating and refining multiple existing corpora, focusing on entities that can be visually interpreted in associated medical images. This resource aims to bridge the gap between natural language processing and computer vision in the biomedical domain. The study evaluates various NER methods, including encoder-only, encoder-decoder, and decoder-only architectures. It explores fine-tuning, zero-shot, and few-shot In-Context Learning (ICL) strategies to determine the most effective approach for entity extraction. The resulting dataset is publicly available."
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%0 Conference Proceedings
%T Extracting Medical Image-Related Entities from Spanish Electronic Health Records Using NER Methods
%A Platas, Alexander
%A Merino, Marcos
%A Zotova, Elena
%A Cuadros, Montse
%A López-Linares, Karen
%A Pérez de Mendiola, Mikel
%A Gálvez, María
%A Barba, Cristina
%A Asla, Antón
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F platas-etal-2026-extracting
%X This paper presents a novel corpus in Spanish tailored for the extraction of medical image-related entities from radiological reports using Named Entity Recognition (NER) methods. The dataset was created by aggregating and refining multiple existing corpora, focusing on entities that can be visually interpreted in associated medical images. This resource aims to bridge the gap between natural language processing and computer vision in the biomedical domain. The study evaluates various NER methods, including encoder-only, encoder-decoder, and decoder-only architectures. It explores fine-tuning, zero-shot, and few-shot In-Context Learning (ICL) strategies to determine the most effective approach for entity extraction. The resulting dataset is publicly available.
%R 10.63317/4t6agzu5ygqr
%U https://aclanthology.org/2026.lrec-1.829/
%U https://doi.org/10.63317/4t6agzu5ygqr
%P 10569-10578
Markdown (Informal)
[Extracting Medical Image-Related Entities from Spanish Electronic Health Records Using NER Methods](https://aclanthology.org/2026.lrec-1.829/) (Platas et al., LREC 2026)
ACL
- Alexander Platas, Marcos Merino, Elena Zotova, Montse Cuadros, Karen López-Linares, Mikel Pérez de Mendiola, María Gálvez, Cristina Barba, and Antón Asla. 2026. Extracting Medical Image-Related Entities from Spanish Electronic Health Records Using NER Methods. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 10569–10578, Palma de Mallorca, Spain. ELRA Language Resource Association.