@inproceedings{kim-etal-2023-ku,
    title = "{KU}-{DMIS}-{MSRA} at {R}ad{S}um23: Pre-trained Vision-Language Model for Radiology Report Summarization",
    author = "Kim, Gangwoo  and
      Kim, Hajung  and
      Ji, Lei  and
      Bae, Seongsu  and
      Kim, Chanhwi  and
      Sung, Mujeen  and
      Kim, Hyunjae  and
      Yan, Kun  and
      Chang, Eric  and
      Kang, Jaewoo",
    editor = "Demner-fushman, Dina  and
      Ananiadou, Sophia  and
      Cohen, Kevin",
    booktitle = "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://aclanthology.org/2023.bionlp-1.59",
    doi = "10.18653/v1/2023.bionlp-1.59",
    pages = "567--573",
    abstract = "In this paper, we introduce CheXOFA, a new pre-trained vision-language model (VLM) for the chest X-ray domain. Our model is initially pre-trained on various multimodal datasets within the general domain before being transferred to the chest X-ray domain. Following a prominent VLM, we unify various domain-specific tasks into a simple sequence-to-sequence schema. It enables the model to effectively learn the required knowledge and skills from limited resources in the domain. Demonstrating superior performance on the benchmark datasets provided by the BioNLP shared task (Delbrouck et al., 2023), our model benefits from its training across multiple tasks and domains. With subtle techniques including ensemble and factual calibration, our system achieves first place on the RadSum23 leaderboard for the hidden test set.",
}

In this paper, we introduce CheXOFA, a new pre-trained vision-language model (VLM) for the chest X-ray domain. Our model is initially pre-trained on various multimodal datasets within the general domain before being transferred to the chest X-ray domain. Following a prominent VLM, we unify various domain-specific tasks into a simple sequence-to-sequence schema. It enables the model to effectively learn the required knowledge and skills from limited resources in the domain. Demonstrating superior performance on the benchmark datasets provided by the BioNLP shared task (Delbrouck et al., 2023), our model benefits from its training across multiple tasks and domains. With subtle techniques including ensemble and factual calibration, our system achieves first place on the RadSum23 leaderboard for the hidden test set.
Kim, Gangwoo Kim, Hajung Ji, Lei Bae, Seongsu Kim, Chanhwi Sung, Mujeen Kim, Hyunjae Yan, Kun Chang, Eric Kang, Jaewoo
KU-DMIS-MSRA at RadSum23: Pre-trained Vision-Language Model for Radiology Report Summarization