Contrastive learning has proven effective for pre-training image models on
unlabeled data with promising results for tasks such as medical image
classification. Using paired text (like radiological reports) during
pre-training improves the results even further. Still, most existing methods
target image classification downstream tasks and may not be optimal for
localized tasks like semantic segmentation or object detection. We therefore
propose Localized representation learning from Vision and Text (LoVT), to our
best knowledge, the first text-supervised pre-training method that targets
localized medical imaging tasks. Our method combines instance-level
image-report contrastive learning with local contrastive learning on image
region and report sentence representations. We evaluate LoVT and commonly used
pre-training methods on an evaluation framework of 18 localized tasks on chest
X-rays from five public datasets. LoVT performs best on 10 of the 18 studied
tasks making it the preferred method of choice for localized tasks.

LoVT 是一种面向医学图像的局部表征学习方法，结合基于实例的图像报告对比学习和图像区域及报告句子表征的局部对比学习，针对局部任务优化图像预先训练，相比其他常用预训练方法，在 18 个局部任务的评估框架中表现最佳，因此被认为是优选方法。