@inproceedings{nicolson-etal-2023-e,
    title = "e-Health {CSIRO} at {R}ad{S}um23: Adapting a Chest {X}-Ray Report Generator to Multimodal Radiology Report Summarisation",
    author = "Nicolson, Aaron  and
      Dowling, Jason  and
      Koopman, Bevan",
    editor = "Demner-fushman, Dina  and
      Ananiadou, Sophia  and
      Cohen, Kevin",
    booktitle = "Proceedings of the 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2023.bionlp-1.56/",
    doi = "10.18653/v1/2023.bionlp-1.56",
    pages = "545--549",
    abstract = "We describe the participation of team e-Health CSIRO in the BioNLP RadSum task of 2023. This task aims to develop automatic summarisation methods for radiology. The subtask that we participated in was multimodal; the impression section of a report was to be summarised from a given findings section and set of Chest X-rays (CXRs) of a subject{'}s study. For our method, we adapted an encoder-to-decoder model for CXR report generation to the subtask. e-Health CSIRO placed seventh amongst the participating teams with a RadGraph ER F1 score of 23.9."
}