59 citations · 224 across the 17 of their papers we have counts for
19 papers
What do neural networks learn in image classification? A frequency shortcut perspective
Shunxin Wang, Raymond Veldhuis, Christoph Brune +1
Frequency analysis is useful for understanding the mechanisms of representation learning in neural networks (NNs). Most research in this area focuses on the learning dynamics of NN…
Uncertainty-based quality assurance of carotid artery wall segmentation in black-blood MRI
Elina Thibeau-Sutre, Dieuwertje Alblas, Sophie Buurman +2
The application of deep learning models to large-scale data sets requires means for automatic quality assurance. We have previously developed a fully automatic algorithm for caroti…
Defocus Blur Synthesis and Deblurring via Interpolation and Extrapolation in Latent Space
Ioana Mazilu, Shunxin Wang, Sven Dummer +3
Though modern microscopes have an autofocusing system to ensure optimal focus, out-of-focus images can still occur when cells within the medium are not all in the same focal plane,…
RDA-INR: Riemannian Diffeomorphic Autoencoding via Implicit Neural Representations
Sven Dummer, Nicola Strisciuglio, Christoph Brune
Diffeomorphic registration frameworks such as Large Deformation Diffeomorphic Metric Mapping (LDDMM) are used in computer graphics and the medical domain for atlas building, statis…
A Survey on the Robustness of Computer Vision Models against Common Corruptions
Shunxin Wang, Raymond Veldhuis, Christoph Brune +1
The performance of computer vision models are susceptible to unexpected changes in input images caused by sensor errors or extreme imaging environments, known as common corruptions…
Implicit Neural Representations for Modeling of Abdominal Aortic Aneurysm Progression
Dieuwertje Alblas, Marieke Hofman, Christoph Brune +2
Abdominal aortic aneurysms (AAAs) are progressive dilatations of the abdominal aorta that, if left untreated, can rupture with lethal consequences. Imaging-based patient monitoring…
SE(3) symmetry lets graph neural networks learn arterial velocity estimation from small datasets
Julian Suk, Christoph Brune, Jelmer M. Wolterink
Hemodynamic velocity fields in coronary arteries could be the basis of valuable biomarkers for diagnosis, prognosis and treatment planning in cardiovascular disease. Velocity field…
Mesh Neural Networks for SE(3)-Equivariant Hemodynamics Estimation on the Artery Wall
Julian Suk, Pim de Haan, Phillip Lippe +2
Computational fluid dynamics (CFD) is a valuable asset for patient-specific cardiovascular-disease diagnosis and prognosis, but its high computational demands hamper its adoption i…
Going Off-Grid: Continuous Implicit Neural Representations for 3D Vascular Modeling
Dieuwertje Alblas, Christoph Brune, Kak Khee Yeung +1
Personalised 3D vascular models are valuable for diagnosis, prognosis and treatment planning in patients with cardiovascular disease. Traditionally, such models have been construct…
Super-Resolved Microbubble Localization in Single-Channel Ultrasound RF Signals Using Deep Learning
Nathan Blanken, Jelmer M. Wolterink, Hervé Delingette +3
Recently, super-resolution ultrasound imaging with ultrasound localization microscopy (ULM) has received much attention. However, ULM relies on low concentrations of microbubbles i…
Deep Learning-Based Carotid Artery Vessel Wall Segmentation in Black-Blood MRI Using Anatomical Priors
Dieuwertje Alblas, Christoph Brune, Jelmer M. Wolterink
Carotid artery vessel wall thickness measurement is an essential step in the monitoring of patients with atherosclerosis. This requires accurate segmentation of the vessel wall, i.…
Mesh convolutional neural networks for wall shear stress estimation in 3D artery models
Julian Suk, Pim de Haan, Phillip Lippe +2
Computational fluid dynamics (CFD) is a valuable tool for personalised, non-invasive evaluation of hemodynamics in arteries, but its complexity and time-consuming nature prohibit l…
Learned SVD: solving inverse problems via hybrid autoencoding
Yoeri E. Boink, Christoph Brune
Our world is full of physics-driven data where effective mappings between data manifolds are desired. There is an increasing demand for understanding combined model-based and data-…
A Partially Learned Algorithm for Joint Photoacoustic Reconstruction and Segmentation
Yoeri E. Boink, Srirang Manohar, Christoph Brune
In an inhomogeneously illuminated photoacoustic image, important information like vascular geometry is not readily available when only the initial pressure is reconstructed. To obt…
Directional Sinogram Inpainting for Limited Angle Tomography
Robert Tovey, Martin Benning, Christoph Brune +5
In this paper we propose a new joint model for the reconstruction of tomography data under limited angle sampling regimes. In many applications of Tomography, e.g. Electron Microsc…
A Framework for Directional and Higher-Order Reconstruction in Photoacoustic Tomography
Yoeri E. Boink, Marinus J. Lagerwerf, Wiendelt Steenbergen +3
Photoacoustic tomography is a hybrid imaging technique that combines high optical tissue contrast with high ultrasound resolution. Direct reconstruction methods such as filtered ba…
Combining Contrast Invariant L1 Data Fidelities with Nonlinear Spectral Image Decomposition
Leonie Zeune, Stephan A. van Gils, Leon W. M. M. Terstappen +1
This paper focuses on multi-scale approaches for variational methods and corresponding gradient flows. Recently, for convex regularization functionals such as total variation, new…
Risk Estimators for Choosing Regularization Parameters in Ill-Posed Problems - Properties and Limitations
Felix Lucka, Katharina Proksch, Christoph Brune +4
This paper discusses the properties of certain risk estimators recently proposed to choose regularization parameters in ill-posed problems. A simple approach is Stein's unbiased ri…
Multiscale Segmentation via Bregman Distances and Nonlinear Spectral Analysis
Leonie Zeune, Guus van Dalum, Leon W. M. M. Terstappen +2
In biomedical imaging reliable segmentation of objects (e.g. from small cells up to large organs) is of fundamental importance for automated medical diagnosis. New approaches for m…