@inproceedings{tagawa-etal-2025-finding,
    title = "Finding-Centric Structuring of {J}apanese Radiology Reports and Analysis of Performance Gaps for Multiple Facilities",
    author = "Tagawa, Yuki  and
      Momoki, Yohei  and
      Nakano, Norihisa  and
      Ozaki, Ryota  and
      Taniguchi, Motoki  and
      Hori, Masatoshi  and
      Tomiyama, Noriyuki",
    editor = "Chen, Weizhu  and
      Yang, Yi  and
      Kachuee, Mohammad  and
      Fu, Xue-Yong",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-industry.7/",
    pages = "70--85",
    ISBN = "979-8-89176-194-0",
    abstract = "This study addresses two key challenges in structuring radiology reports: the lack of a practical structuring schema and datasets to evaluate model generalizability. To address these challenges, we propose a ``Finding-Centric Structuring,'' which organizes reports around individual findings, facilitating secondary use. We also construct JRadFCS, a large-scale dataset with annotated named entities (NEs) and relations, comprising 8,428 Japanese Computed Tomography (CT) reports from seven facilities, providing a comprehensive resource for evaluating model generalizability. Our experiments reveal performance gaps when applying models trained on single-facility reports to those from other facilities. We further analyze factors contributing to these gaps and demonstrate that augmenting the training set based on these performance-correlated factors can efficiently enhance model generalizability."
}