  The development of medical vision-language foundation models has attracted
significant attention in the field of medicine and healthcare due to their
promising prospect in various clinical applications. While previous studies
have commonly focused on feature learning at a single learning scale,
investigation on integrating multi-scale information is lacking, which may
hinder the potential for mutual reinforcement among these features. This paper
aims to bridge this gap by proposing a method that effectively exploits
multi-scale information to enhance the performance of medical foundation
models. The proposed method simultaneously exploits features at the local,
instance, modality and global aspects, facilitating comprehensive
representation learning within the models. We evaluate the effectiveness of the
proposed method on six open-source datasets across different clinical tasks,
demonstrating its ability to enhance the performance of medical foundation
models.
