A conditional diffusion model with a per-timestep discriminator, spatial attention, and latent embedding reports state-of-the-art accuracy on three medical segmentation datasets using only 2 to 4 diffusion steps.
& Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation.International Con- ference On Medical Image Computing And Computer-assisted Intervention
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Conditional diffusion model with spatial attention and latent embedding for medical image segmentation
A conditional diffusion model with a per-timestep discriminator, spatial attention, and latent embedding reports state-of-the-art accuracy on three medical segmentation datasets using only 2 to 4 diffusion steps.