  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.
