Analysing Diffusion Segmentation for Medical Images

arXiv:2403.14440v1 Announce Type: cross
Abstract: Denoising Diffusion Probabilistic models have become increasingly popular due to their ability to offer probabilistic modeling and generate diverse outputs. This versatility inspired their adaptation for image segmentation, where multiple predictions of the model can produce segmentation results that not only achieve high quality but also capture the uncertainty inherent in the model. Here, powerful architectures were proposed for improving diffusion segmentation performance. However, there is a notable lack of analysis and discussions on the …

abstract arxiv become cs.cv cs.lg denoising diffusion diverse eess.iv generate image images medical modeling multiple popular predictions probabilistic modeling quality results segmentation type uncertainty