Medical generative models, acknowledged for their high-quality sample
generation ability, have accelerated the fast growth of medical applications.
However, recent works concentrate on separate medical generation models for
distinct medical tasks and are restricted to inadequate medical multi-modal
knowledge, constraining medical comprehensive diagnosis. In this paper, we
propose MedM2G, a Medical Multi-Modal Generative framework, with the key
innovation to align, extract, and generate medical multi-modal within a unified
model. Extending beyond single or two medical modalities, we efficiently align
medical multi-modal through the central alignment approach in the unified
space. Significantly, our framework extracts valuable clinical knowledge by
preserving the medical visual invariant of each imaging modal, thereby
enhancing specific medical information for multi-modal generation. By
conditioning the adaptive cross-guided parameters into the multi-flow diffusion
framework, our model promotes flexible interactions among medical multi-modal
for generation. MedM2G is the first medical generative model that unifies
medical generation tasks of text-to-image, image-to-text, and unified
generation of medical modalities (CT, MRI, X-ray). It performs 5 medical
generation tasks across 10 datasets, consistently outperforming various
state-of-the-art works.

MedM2G 是一个医疗多模态生成框架，通过统一的模型实现医疗多模态对齐、提取和生成，以及在医疗诊断中提升特定医疗信息和灵活的多模态交互，成功完成了文本到图像、图像到文本和医学模态的统一生成任务，并在 10 个数据集上持续领先于各种最先进的方法。