  In recent years, the growing demand for medical imaging diagnosis has placed
a significant burden on radiologists. As a solution, Medical Vision-Language
Pre-training (Med-VLP) methods have been proposed to learn universal
representations from medical images and reports, benefiting downstream tasks
without requiring fine-grained annotations. However, existing methods have
overlooked the importance of cross-modal alignment in joint image-text
reconstruction, resulting in insufficient cross-modal interaction. To address
this limitation, we propose a unified Med-VLP framework based on Multi-task
Paired Masking with Alignment (MPMA) to integrate the cross-modal alignment
task into the joint image-text reconstruction framework to achieve more
comprehensive cross-modal interaction, while a Global and Local Alignment (GLA)
module is designed to assist self-supervised paradigm in obtaining semantic
representations with rich domain knowledge. Furthermore, we introduce a
Memory-Augmented Cross-Modal Fusion (MA-CMF) module to fully integrate visual
information to assist report reconstruction and fuse the multi-modal
representations adequately. Experimental results demonstrate that the proposed
unified approach outperforms previous methods in all downstream tasks,
including uni-modal, cross-modal, and multi-modal tasks.
