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20212023
most citedDiffusion Models for Implicit Image Segmentation Ensembles

71 citations · 74 across the 5 of their papers we have counts for

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5 papers

eess.IV20233 cited

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…

eess.IV2023

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…

cs.CV2023

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…

eess.IV2022

Ensemble uncertainty as a criterion for dataset expansion in distinct bone segmentation from upper-body CT images

Eva Schnider, Antal Huck, Mireille Toranelli +4

Purpose: The localisation and segmentation of individual bones is an important preprocessing step in many planning and navigation applications. It is, however, a time-consuming and…

cs.CV202171 cited

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…