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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 variousmultimodal datasets within the general domain before being transferred to thechest X-ray domain. Following a prominent VLM, we unify various domain-specifictasks into a simple sequence-to-sequence schema. It enables the model toeffectively learn the required knowledge and skills from limited resources inthe domain. Demonstrating superior performance on the benchmark datasetsprovided by the BioNLP shared task, our model benefits from its training acrossmultiple tasks and domains. With subtle techniques including ensemble andfactual calibration, our system achieves first place on the RadSum23leaderboard for the hidden test set.

KU-DMIS-MSRA at RadSum23: Pre-trained Vision-Language Model for Radiology Report Summarization