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With the availability of large-scale, comprehensive, and general-purposevision-language (VL) datasets such as MSCOCO, vision-language pre-training(VLP) has become an active area of research and proven to be effective forvarious VL tasks such as visual-question answering. However, studies on VLP inthe medical domain have so far been scanty. To provide a comprehensiveperspective on VLP for medical VL tasks, we conduct a thorough experimentalanalysis to study key factors that may affect the performance of VLP with aunified vision-language Transformer. To allow making sound and quickpre-training decisions, we propose RadioGraphy Captions (RGC), a high-quality,multi-modality radiographic dataset containing 18,434 image-caption pairscollected from an open-access online database MedPix. RGC can be used as apre-training dataset or a new benchmark for medical report generation andmedical image-text retrieval. By utilizing RGC and other available datasets forpre-training, we develop several key insights that can guide future medical VLPresearch and new strong baselines for various medical VL tasks.

Multi-modal Pre-training for Medical Vision-language Understanding and Generation: An Empirical Study with A New Benchmark