Medical Vision Language Pretraining (VLP) has recently emerged as a promising
solution to the scarcity of labeled data in the medical domain. By leveraging
paired/unpaired vision and text datasets through self-supervised learning,
models can be trained to acquire vast knowledge and learn robust feature
representations. Such pretrained models have the potential to enhance multiple
downstream medical tasks simultaneously, reducing the dependency on labeled
data. However, despite recent progress and its potential, there is no such
comprehensive survey paper that has explored the various aspects and
advancements in medical VLP. In this paper, we specifically review existing
works through the lens of different pretraining objectives, architectures,
downstream evaluation tasks, and datasets utilized for pretraining and
downstream tasks. Subsequently, we delve into current challenges in medical
VLP, discussing existing and potential solutions, and conclude by highlighting
future directions. To the best of our knowledge, this is the first survey
focused on medical VLP.

医疗视觉语言预训练透过自监督学习的方式利用图像和文本数据集，为医学领域稀缺的标注数据提供了解决方案。该文回顾了医疗视觉语言预训练的不同目标、架构、评估任务和数据集，并探讨了现有挑战及未来发展方向。