  Existing contrastive language-image pre-training aims to learn a joint
representation by matching abundant image-text pairs. However, the number of
image-text pairs in medical datasets is usually orders of magnitude smaller
than that in natural datasets. Besides, medical image-text pairs often involve
numerous complex fine-grained correspondences. This paper aims to enhance the
data efficiency by introducing multiple-to-multiple local relationship modeling
to capture denser supervisions. More specifically, we propose a Medical
Language-Image Pre-training (MLIP) framework, which exploits the limited
image-text medical data more efficiently through patch-sentence matching.
Furthermore, we introduce a masked contrastive learning strategy with semantic
integrity estimation to reduce redundancy in images while preserving the
underlying semantics. Our evaluation results show that MLIP outperforms
previous work in zero/few-shot classification and few-shot segmentation tasks
by a large margin.
