Search Results for author:
Found 21 papers, 12 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.
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 secret role of undesired physical effects in accurate shape sensing with eccentric FBGs
These findings imply that deep learning techniques benefit from the bending-induced effects that impact the desired signal in a complex manner.
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.
Comparison of Methods Generalizing Max- and Average-Pooling
Max- and average-pooling are the most popular pooling methods for downsampling in convolutional neural networks.
3D Segmentation Networks for Excessive Numbers of Classes: Distinct Bone Segmentation in Upper Bodies
Segmentation of distinct bones plays a crucial role in diagnosis, planning, navigation, and the assessment of bone metastasis.
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.
Accelerated Motion-Aware MR Imaging via Motion Prediction from K-Space Center
A recently proposed MR imaging approach copes with motion by observing the motion patterns during the acquisition.
Recurrent Registration Neural Networks for Deformable Image Registration
We trained our network on 2D magnetic resonance images of the lung and compared our method to a standard parametric B-spline registration.
AirLab: Autograd Image Registration Laboratory
With the "Autograd Image Registration Laboratory" (AIRLab), we introduce an open laboratory for image registration tasks, where the analytic gradients of the objective function are computed automatically and the device where the computations are performed, on a CPU or a GPU, is transparent.
Multi-dimensional Gated Recurrent Units for Automated Anatomical Landmark Localization
We present an automated method for localizing an anatomical landmark in three-dimensional medical images.
