  Medical image segmentation is a challenging task, made more difficult by many
datasets' limited size and annotations. Denoising diffusion probabilistic
models (DDPM) have recently shown promise in modelling the distribution of
natural images and were successfully applied to various medical imaging tasks.
This work focuses on semi-supervised image segmentation using diffusion models,
particularly addressing domain generalisation. Firstly, we demonstrate that
smaller diffusion steps generate latent representations that are more robust
for downstream tasks than larger steps. Secondly, we use this insight to
propose an improved esembling scheme that leverages information-dense small
steps and the regularising effect of larger steps to generate predictions. Our
model shows significantly better performance in domain-shifted settings while
retaining competitive performance in-domain. Overall, this work highlights the
potential of DDPMs for semi-supervised medical image segmentation and provides
insights into optimising their performance under domain shift.
