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Existing contrastive language-image pre-training aims to learn a jointrepresentation by matching abundant image-text pairs. However, the number ofimage-text pairs in medical datasets is usually orders of magnitude smallerthan that in natural datasets. Besides, medical image-text pairs often involvenumerous complex fine-grained correspondences. This paper aims to enhance thedata efficiency by introducing multiple-to-multiple local relationship modelingto capture denser supervisions. More specifically, we propose a MedicalLanguage-Image Pre-training (MLIP) framework, which exploits the limitedimage-text medical data more efficiently through patch-sentence matching.Furthermore, we introduce a masked contrastive learning strategy with semanticintegrity estimation to reduce redundancy in images while preserving theunderlying semantics. Our evaluation results show that MLIP outperformsprevious work in zero/few-shot classification and few-shot segmentation tasksby a large margin.

MLIP: Medical Language-Image Pre-training with Masked Local Representation Learning