  In the realm of smart healthcare, researchers enhance the scale and diversity
of medical datasets through medical image synthesis. However, existing methods
are limited by CNN local perception and Transformer quadratic complexity,
making it difficult to balance structural texture consistency. To this end, we
propose the Vision Mamba DDPM (VM-DDPM) based on State Space Model (SSM), fully
combining CNN local perception and SSM global modeling capabilities, while
maintaining linear computational complexity. Specifically, we designed a
multi-level feature extraction module called Multi-level State Space Block
(MSSBlock), and a basic unit of encoder-decoder structure called State Space
Layer (SSLayer) for medical pathological images. Besides, we designed a simple,
Plug-and-Play, zero-parameter Sequence Regeneration strategy for the Cross-Scan
Module (CSM), which enabled the S6 module to fully perceive the spatial
features of the 2D image and stimulate the generalization potential of the
model. To our best knowledge, this is the first medical image synthesis model
based on the SSM-CNN hybrid architecture. Our experimental evaluation on three
datasets of different scales, i.e., ACDC, BraTS2018, and ChestXRay, as well as
qualitative evaluation by radiologists, demonstrate that VM-DDPM achieves
state-of-the-art performance.
