@inproceedings{sun-etal-2026-multimodal,
    title = "Multimodal Dual-Path Decoding for Medical Report Generation",
    author = "Sun, Jinghan  and
      Wei, Dong  and
      Zhu, Zhihong  and
      Xue, Yuyang  and
      McDonagh, Steven  and
      Wu, Xian",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1997/",
    pages = "40193--40204",
    ISBN = "979-8-89176-395-1",
    abstract = "Radiology report generation requires precise alignment between medical imaging findings and clinically coherent textual descriptions. While current methods predominantly rely on either large vision-language models (LVLMs) for visual grounding or large language models (LLMs) for medical narrative generation, they often fail to effectively integrate multimodal clinical evidence with domain-specific knowledge. This paper proposes a novel multimodal dual-path framework that synergistically combines LVLMs and LLMs to address these limitations. Our approach establishes a dynamic fusion between LVLMs' visual-semantic grounding capabilities and LLMs' clinical knowledge reasoning. Specifically, we employ a structured prompting strategy that models the report generation task into three clinically meaningful sections and introduces fine-grained multi-label classification prompts to guide the models, enabling more accurate and comprehensive clinical report generation. Experiments on the public MIMIC-CXR benchmark demonstrate our framework{'}s superiority over state-of-the-art methods."
}