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Recently, multi-modal vision-language foundation models have gainedsignificant attention in the medical field. While these models offer greatopportunities, they still face a number of challenges, such as the requirementfor fine-grained knowledge understanding in computer-aided diagnosis andcapability of utilizing very limited or no task-specific labeled data inreal-world clinical applications. In this study, we present MaCo, a novelmulti-modal medical foundation model that explores masked contrastive learningto achieve granular alignment and zero-shot learning for a variety of medicalimaging tasks. MaCo incorporates a correlation weighting mechanism to adjustthe correlation between masked image patches and their corresponding reports,thereby enhancing the representation learning capabilities. We evaluate MaCo onsix well-known open-source X-ray datasets, and the experimental results show itoutperforms seven state-of-the-art approaches for classification, segmentation,and zero-shot phase grounding, demonstrating its great potential to promote awide range of medical image analysis tasks.

Enhancing Representation in Radiography-Reports Foundation Model: A Granular Alignment Algorithm Using Masked Contrastive Learning