  The Diffusion Probabilistic Model (DPM) has recently gained popularity in the
field of computer vision, thanks to its image generation applications, such as
Imagen, Latent Diffusion Models, and Stable Diffusion, which have demonstrated
impressive capabilities and sparked much discussion within the community.
Recent investigations have further unveiled the utility of DPM in the domain of
medical image analysis, as underscored by the commendable performance exhibited
by the medical image segmentation model across various tasks. Although these
models were originally underpinned by a UNet architecture, there exists a
potential avenue for enhancing their performance through the integration of
vision transformer mechanisms. However, we discovered that simply combining
these two models resulted in subpar performance. To effectively integrate these
two cutting-edge techniques for the Medical image segmentation, we propose a
novel Transformer-based Diffusion framework, called MedSegDiff-V2. We verify
its effectiveness on 20 medical image segmentation tasks with different image
modalities. Through comprehensive evaluation, our approach demonstrates
superiority over prior state-of-the-art (SOTA) methodologies. Code is released
at https://github.com/KidsWithTokens/MedSegDiff
