41 citations · 67 across the 13 of their papers we have counts for
6 papers · 1 filter
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…
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…