KU-DMIS-MSRA at RadSum23: Pre-trained Vision-Language Model for
  Radiology Report Summarization
Gangwoo Kim Hajung Kim Lei Ji Seongsu Bae Chanhwi Kim Mujeen Sung Hyunjae Kim Kun Yan Eric Chang Jaewoo Kang
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, 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.