  Recently, medical vision-language pre-training (VLP) has reached substantial
progress to learn global visual representation from medical images and their
paired radiology reports. However, medical imaging tasks in real world usually
require finer granularity in visual features. These tasks include visual
localization tasks (e.g., semantic segmentation, object detection) and visual
grounding task. Yet, current medical VLP methods face challenges in learning
these fine-grained features, as they primarily focus on brute-force alignment
between image patches and individual text tokens for local visual feature
learning, which is suboptimal for downstream dense prediction tasks. In this
work, we propose a new VLP framework, named \textbf{G}lobal to \textbf{D}ense
level representation learning (G2D) that achieves significantly improved
granularity and more accurate grounding for the learned features, compared to
existing medical VLP approaches. In particular, G2D learns dense and
semantically-grounded image representations via a pseudo segmentation task
parallel with the global vision-language alignment. Notably, generating pseudo
segmentation targets does not incur extra trainable parameters: they are
obtained on the fly during VLP with a parameter-free processor. G2D achieves
superior performance across 6 medical imaging tasks and 25 diseases,
particularly in semantic segmentation, which necessitates fine-grained,
semantically-grounded image features. In this task, G2D surpasses peer models
even when fine-tuned with just 1\% of the training data, compared to the 100\%
used by these models. The code will be released upon acceptance.
