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Found 6 papers, 3 papers with code
Denoising Diffusion Models for 3D Healthy Brain Tissue Inpainting
Monitoring diseases that affect the brain's structural integrity requires automated analysis of magnetic resonance (MR) images, e. g., for the evaluation of volumetric changes.
Binary Noise for Binary Tasks: Masked Bernoulli Diffusion for Unsupervised Anomaly Detection
As diffusion-based methods require a lot of GPU memory and have long sampling times, we present a novel and fast unsupervised anomaly detection approach based on latent Bernoulli diffusion models.
WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image Synthesis
Due to the three-dimensional nature of CT- or MR-scans, generative modeling of medical images is a particularly challenging task.
Denoising Diffusion Models for Inpainting of Healthy Brain Tissue
We use a 2D model that is trained using slices in which healthy tissue was cropped out and is learned to be inpainted again.
Diffusion Models for Memory-efficient Processing of 3D Medical Images
Denoising diffusion models have recently achieved state-of-the-art performance in many image-generation tasks.
Diffusion Models for Contrast Harmonization of Magnetic Resonance Images
We map images from the source contrast to the target contrast for both directions, from 3 T to 1. 5 T and from 1. 5 T to 3 T. As we only want to change the contrast, not the anatomical information, our method uses the original image to guide the image-to-image translation process by adding structural information.
