33 citations · 68 across the 12 of their papers we have counts for
12 papers
zea: A Toolbox for Cognitive Ultrasound Imaging
Tristan S. W. Stevens, Wessel L. van Nierop, Ben Luijten +7
We present zea (pronounced ze-yah), a Python package for cognitive ultrasound imaging that offers a flexible, modular, and differentiable pipeline for ultrasound data processing. A…
SVD-NO: Learning PDE Solution Operators with SVD Integral Kernels
Noam Koren, Ralf J. J. Mackenbach, Ruud J. G. van Sloun +2
Neural operators have emerged as a promising paradigm for learning solution operators of partial differential equa- tions (PDEs) directly from data. Existing methods, such as those…
Adaptive Bayesian Single-Shot Quantum Sensing
Ivana Nikoloska, Ruud Van Sloun, Osvaldo Simeone
Quantum sensing harnesses the unique properties of quantum systems to enable precision measurements of physical quantities such as time, magnetic and electric fields, acceleration,…
EOTNet: Deep Memory Aided Bayesian Filter for Extended Object Tracking
Zhixing Wang, Le Zheng, Shi Yan +3
Extended object tracking methods based on random matrices, founded on Bayesian filters, have been able to achieve efficient recursive processes while jointly estimating the kinemat…
Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging
Tristan S. W. Stevens, Jeroen Overdevest, Oisín Nolan +3
Deep generative models have been studied and developed primarily in the context of natural images and computer vision. This has spurred the development of (Bayesian) methods that u…
Active inference and deep generative modeling for cognitive ultrasound
Ruud JG van Sloun
Ultrasound (US) has the unique potential to offer access to medical imaging to anyone, everywhere. Devices have become ultra-portable and cost-effective, akin to the stethoscope. N…
WAND: Wavelet Analysis-based Neural Decomposition of MRS Signals for Artifact Removal
Julian P. Merkofer, Dennis M. J. van de Sande, Sina Amirrajab +3
Accurate quantification of metabolites in magnetic resonance spectroscopy (MRS) is challenged by low signal-to-noise ratio (SNR), overlapping metabolites, and various artifacts. Pa…
Learning Structured Compressed Sensing with Automatic Resource Allocation
Han Wang, Eduardo Pérez, Iris A. M. Huijben +3
Multidimensional data acquisition often requires extensive time and poses significant challenges for hardware and software regarding data storage and processing. Rather than design…
HKF: Hierarchical Kalman Filtering with Online Learned Evolution Priors for Adaptive ECG Denoising
Guy Revach, Timur Locher, Nir Shlezinger +2
Electrocardiography (ECG) signals play a pivotal role in many healthcare applications, especially in at-home monitoring of vital signs. Wearable technologies, which these applicati…
Deep Unfolding with Normalizing Flow Priors for Inverse Problems
Xinyi Wei, Hans van Gorp, Lizeth Gonzalez Carabarin +3
Many application domains, spanning from computational photography to medical imaging, require recovery of high-fidelity images from noisy, incomplete or partial/compressed measurem…
Adaptive Ultrasound Beamforming using Deep Learning
Ben Luijten, Regev Cohen, Frederik J. de Bruijn +4
Biomedical imaging is unequivocally dependent on the ability to reconstruct interpretable and high-quality images from acquired sensor data. This reconstruction process is pivotal…
Synthetic Elastography using B-mode Ultrasound through a Deep Fully-Convolutional Neural Network
R. R. Wildeboer, R. J. G. van Sloun, C. K. Mannaerts +5
Shear-wave elastography (SWE) permits local estimation of tissue elasticity, an important imaging marker in biomedicine. This recently-developed, advanced technique assesses the sp…