@inproceedings{munkhoeva-etal-2024-airi,
    title = "{AIRI} at {RRG}24: {LL}a{V}a with specialised encoder and decoder",
    author = "Munkhoeva, Marina  and
      Umerenkov, Dmitry  and
      Samokhin, Valentin",
    editor = "Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Miwa, Makoto  and
      Roberts, Kirk  and
      Tsujii, Junichi",
    booktitle = "Proceedings of the 23rd Workshop on Biomedical Natural Language Processing",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2024.bionlp-1.51/",
    doi = "10.18653/v1/2024.bionlp-1.51",
    pages = "603--607",
    abstract = "We present a new approach to generating the `Findings' and `Impression' sections in the chest X-rays radiology reports, developed as part of the shared radiology task at BioNLP 2024. By integrating a DINOv2 vision encoder trained on medical data with specialized biomedical large language model using the LLaVA framework, our method addresses complex medical semantics and diverse findings in imaging. We use datasets from PadChest, BIMCV-COVID19, CheXpert, OpenI, and MIMIC-CXR. The evaluation metrics demonstrate our method{'}s effectiveness and the potential for automating the generation of radiology reports."
}