@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://preview.aclanthology.org/ingest-nlpsi/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."
}