The development of multi-modal medical foundation models has attracted
significant attention in the field of medicine and healthcare due to their
promising prospects in various clinical applications. One area of focus in this
research direction is the extractions of features at different scales. While
previous studies have explored feature learning at individual scales,
investigation on integrating the diverse scales and modalities of 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 and cross-modality information to enhance the
performance of medical foundation models. The proposed method simultaneously
exploit 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.

提出的方法有效地利用多尺度和跨模态信息来增强医学基础模型的性能，同时结合局部、实例、模态和全局方面的特征，促进了模型内的综合表示学习，通过在六个开源数据集上的评估表明了其增强医学基础模型性能的能力。