Search Results for author:
Found 15 papers, 11 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.
Generative AI for Medical Imaging: extending the MONAI Framework
We have implemented these models in a generalisable fashion, illustrating that their results can be extended to 2D or 3D scenarios, including medical images with different modalities (like CT, MRI, and X-Ray data) and from different anatomical areas.
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.
Point Cloud Diffusion Models for Automatic Implant Generation
Advances in 3D printing of biocompatible materials make patient-specific implants increasingly popular.
Improved distinct bone segmentation in upper-body CT through multi-resolution networks
The approach thus improves the accuracy and efficiency of distinct bone segmentation from upper-body CT.
Position Regression for Unsupervised Anomaly Detection
Most current anomaly detection methods for medical images are based on image reconstruction.
The Swiss Army Knife for Image-to-Image Translation: Multi-Task Diffusion Models
We build on a method for image-to-image translation using denoising diffusion implicit models and include a regression problem and a segmentation problem for guiding the image generation to the desired output.
Diffusion Models for Medical Anomaly Detection
In medical applications, weakly supervised anomaly detection methods are of great interest, as only image-level annotations are required for training.
Diffusion Models for Implicit Image Segmentation Ensembles
By modifying the training and sampling scheme, we show that diffusion models can perform lesion segmentation of medical images.
Learn to Ignore: Domain Adaptation for Multi-Site MRI Analysis
The limited availability of large image datasets, mainly due to data privacy and differences in acquisition protocols or hardware, is a significant issue in the development of accurate and generalizable machine learning methods in medicine.
DeScarGAN: Disease-Specific Anomaly Detection with Weak Supervision
In this work, we present a weakly supervised and detail-preserving method that is able to detect structural changes of existing anatomical structures.
