@inproceedings{sun-etal-2025-fact,
    title = "Fact-Aware Multimodal Retrieval Augmentation for Accurate Medical Radiology Report Generation",
    author = "Sun, Liwen  and
      Zhao, James Jialun  and
      Han, Wenjing  and
      Xiong, Chenyan",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-long.28/",
    pages = "643--655",
    ISBN = "979-8-89176-189-6",
    abstract = "Multimodal foundation models hold significant potential for automating radiology report generation, thereby assisting clinicians in diagnosing cardiac diseases. However, generated reports often suffer from serious factual inaccuracy. In this paper, we introduce a fact-aware multimodal retrieval-augmented pipeline in generating accurate radiology reports (FactMM-RAG). We first leverage RadGraph to mine factual report pairs, then integrate factual knowledge to train a universal multimodal retriever. Given a radiology image, our retriever can identify high-quality reference reports to augment multimodal foundation models, thus enhancing the factual completeness and correctness of report generation. Experiments on two benchmark datasets demonstrate that our multimodal retriever significantly outperforms other state-of-the-art retrievers on both language generation and radiology-specific metrics, up to 6.5{\%} and 2{\%} score in F1CheXbert and F1RadGraph. Further analysis indicates that employing our factually-informed training strategy imposes an effective supervision signal, without relying on explicit diagnostic label guidance, and successfully propagate fact-aware capabilities from the multimodal retriever to the multimodal foundation model in radiology report generation."
}