MedM2G: Unifying Medical Multi-Modal Generation via Cross-Guided Diffusion with Visual Invariant

Chenlu Zhan, Yu Lin, Gaoang Wang, Hongwei Wang, Jian Wu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 11502-11512

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.