71 citations · 224 across the 23 of their papers we have counts for
24 papers
Observation-Conditioned Latent Energy Priors for Sparse Implicit Neural Shape Completion
Paul Büschl, Ezequiel de la Rosa, Julia Wolleb +3
Implicit neural representations (INRs) can model continuous 3D shapes with a shared coordinate decoder and per-instance latent codes. At test time, autodecoder-style models commonl…
Multimodal Assessment of Pancreatic Cancer Resectability Using Deep Learning
Vincent Ochs, Christoph Kuemmerli, Florentin Bieder +12
Accurate determination of pancreatic ductal adenocarcinoma (PDAC) resectability relies on evaluating how the tumor interacts with major peripancreatic vessels on CT imaging, yet ex…
Low-Rank-Modulated Functa: Exploring the Latent Space of Implicit Neural Representations for Interpretable Ultrasound Video Analysis
Julia Wolleb, Cristiana Baloescu, Alicia Durrer +2
Implicit neural representations (INRs) have emerged as a powerful framework for continuous image representation learning. In Functa-based approaches, each image is encoded as a lat…
VidFuncta: Towards Generalizable Neural Representations for Ultrasound Videos
Julia Wolleb, Florentin Bieder, Paul Friedrich +2
Ultrasound is widely used in clinical care, yet standard deep learning methods often struggle with full video analysis due to non-standardized acquisition and operator bias. We off…
MedFuncta: A Unified Framework for Learning Efficient Medical Neural Fields
Paul Friedrich, Florentin Bieder, Julian McGinnis +3
Research in medical imaging primarily focuses on discrete data representations that poorly scale with grid resolution and fail to capture the often continuous nature of the underly…
cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image Synthesis
Paul Friedrich, Alicia Durrer, Julia Wolleb +1
This paper contributes to the "BraTS 2024 Brain MR Image Synthesis Challenge" and presents a conditional Wavelet Diffusion Model (cWDM) for directly solving a paired image-to-image…
Denoising Diffusion Models for Anomaly Localization in Medical Images
Cosmin I. Bercea, Philippe C. Cattin, Julia A. Schnabel +1
This review explores anomaly localization in medical images using denoising diffusion models. After providing a brief methodological background of these models, including their app…
Modeling the Neonatal Brain Development Using Implicit Neural Representations
Florentin Bieder, Paul Friedrich, Hélène Corbaz +3
The human brain undergoes rapid development during the third trimester of pregnancy. In this work, we model the neonatal development of the infant brain in this age range. As a bas…
Denoising Diffusion Models for 3D Healthy Brain Tissue Inpainting
Alicia Durrer, Julia Wolleb, Florentin Bieder +12
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. Howe…
Binary Noise for Binary Tasks: Masked Bernoulli Diffusion for Unsupervised Anomaly Detection
Julia Wolleb, Florentin Bieder, Paul Friedrich +3
The high performance of denoising diffusion models for image generation has paved the way for their application in unsupervised medical anomaly detection. As diffusion-based method…
WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image Synthesis
Paul Friedrich, Julia Wolleb, Florentin Bieder +2
Due to the three-dimensional nature of CT- or MR-scans, generative modeling of medical images is a particularly challenging task. Existing approaches mostly apply patch-wise, slice…
Denoising Diffusion Models for Inpainting of Healthy Brain Tissue
Alicia Durrer, Philippe C. Cattin, Julia Wolleb
This paper is a contribution to the "BraTS 2023 Local Synthesis of Healthy Brain Tissue via Inpainting Challenge". The task of this challenge is to transform tumor tissue into heal…
Generative AI for Medical Imaging: extending the MONAI Framework
Walter H. L. Pinaya, Mark S. Graham, Eric Kerfoot +21
Recent advances in generative AI have brought incredible breakthroughs in several areas, including medical imaging. These generative models have tremendous potential not only to he…
The Brain Tumor Segmentation (BraTS) Challenge: Local Synthesis of Healthy Brain Tissue via Inpainting
Florian Kofler, Felix Meissen, Felix Steinbauer +103
A myriad of algorithms for the automatic analysis of brain MR images is available to support clinicians in their decision-making. For brain tumor patients, the image acquisition ti…
Diffusion Models for Contrast Harmonization of Magnetic Resonance Images
Alicia Durrer, Julia Wolleb, Florentin Bieder +6
Magnetic resonance (MR) images from multiple sources often show differences in image contrast related to acquisition settings or the used scanner type. For long-term studies, longi…
Memory-Efficient 3D Denoising Diffusion Models for Medical Image Processing
Florentin Bieder, Julia Wolleb, Alicia Durrer +2
Denoising diffusion models have recently achieved state-of-the-art performance in many image-generation tasks. They do, however, require a large amount of computational resources.…
Point Cloud Diffusion Models for Automatic Implant Generation
Paul Friedrich, Julia Wolleb, Florentin Bieder +2
Advances in 3D printing of biocompatible materials make patient-specific implants increasingly popular. The design of these implants is, however, still a tedious and largely manual…
Improved distinct bone segmentation in upper-body CT through multi-resolution networks
Eva Schnider, Julia Wolleb, Antal Huck +4
Purpose: Automated distinct bone segmentation from CT scans is widely used in planning and navigation workflows. U-Net variants are known to provide excellent results in supervised…
Position Regression for Unsupervised Anomaly Detection
Florentin Bieder, Julia Wolleb, Robin Sandkühler +1
In recent years, anomaly detection has become an essential field in medical image analysis. Most current anomaly detection methods for medical images are based on image reconstruct…
The Swiss Army Knife for Image-to-Image Translation: Multi-Task Diffusion Models
Julia Wolleb, Robin Sandkühler, Florentin Bieder +1
Recently, diffusion models were applied to a wide range of image analysis tasks. We build on a method for image-to-image translation using denoising diffusion implicit models and i…
Diffusion Models for Medical Anomaly Detection
Julia Wolleb, Florentin Bieder, Robin Sandkühler +1
In medical applications, weakly supervised anomaly detection methods are of great interest, as only image-level annotations are required for training. Current anomaly detection met…
Diffusion Models for Implicit Image Segmentation Ensembles
Julia Wolleb, Robin Sandkühler, Florentin Bieder +2
Diffusion models have shown impressive performance for generative modelling of images. In this paper, we present a novel semantic segmentation method based on diffusion models. By…
Learn to Ignore: Domain Adaptation for Multi-Site MRI Analysis
Julia Wolleb, Robin Sandkühler, Florentin Bieder +7
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 accu…
DeScarGAN: Disease-Specific Anomaly Detection with Weak Supervision
Julia Wolleb, Robin Sandkühler, Philippe C. Cattin
Anomaly detection and localization in medical images is a challenging task, especially when the anomaly exhibits a change of existing structures, e.g., brain atrophy or changes in…