A large-scale image-text pair dataset has greatly contributed to the
development of vision-language pre-training (VLP) models, which enable
zero-shot or few-shot classification without costly annotation. However, in the
medical domain, the scarcity of data remains a significant challenge for
developing a powerful VLP model. In this paper, we tackle the lack of
image-text data in chest X-ray by expanding image-label pair as image-text pair
via general prompt and utilizing multiple images and multiple sections in a
radiologic report. We also design two contrastive losses, named ICL and TCL,
for learning study-level characteristics of medical images and reports,
respectively. Our model outperforms the state-of-the-art models trained under
the same conditions. Also, enlarged dataset improve the discriminative power of
our pre-trained model for classification, while sacrificing marginal retrieval
performance. Code is available at this https URL

本文研究了利用大规模图像 - 文本配对数据集进行视觉语言预训练模型的发展，解决医疗领域中缺乏数据的问题，并通过扩展图像 - 标签对为图像 - 文本对，利用多个图像和多个部分的放射学报告来提高模型性能。同时设计了两个对比损失，ICL 和 TCL，来学习医学图像和报告的研究级特征。我们的模型在相同条件下优于现有的最先进模型。此外，扩大数据集能够提高我们预训练模型的分类性能，尽管在检索性能方面有所牺牲。代码可在此网址获得。