VM-DDPM: Vision Mamba Diffusion for Medical Image Synthesis

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.

HAGAN: Hybrid Augmented Generative Adversarial Network for Medical Image Synthesis

Medical Image Synthesis (MIS) plays an important role in the intelligent medical field, which greatly saves the economic and time costs of medical diagnosis. However, due to the complexity of medical images and similar characteristics of different tissue cells, existing methods face great challenges in meeting their biological consistency. To this end, we propose the Hybrid Augmented Generative Adversarial Network (HAGAN) to maintain the authenticity of structural texture and tissue cells. HAGAN contains Attention Mixed (AttnMix) Generator, Hierarchical Discriminator and Reverse Skip Connection between Discriminator and Generator. The AttnMix consistency differentiable regularization encourages the perception in structural and textural variations between real and fake images, which improves the pathological integrity of synthetic images and the accuracy of features in local areas. The Hierarchical Discriminator introduces pixel-by-pixel discriminant feedback to generator for enhancing the saliency and discriminance of global and local details simultaneously. The Reverse Skip Connection further improves the accuracy for fine details by fusing real and synthetic distribution features. Our experimental evaluations on three datasets of different scales, i.e., COVID-CT, ACDC and BraTS2018, demonstrate that HAGAN outperforms the existing methods and achieves state-of-the-art performance in both high-resolution and low-resolution.