Search Results for author: Ruud J. G. van Sloun

Found 22 papers, 10 papers with code

Hierarchical Filtering with Online Learned Priors for ECG Denoising

1 code implementation23 Oct 2022 Timur Locher, Guy Revach, Nir Shlezinger, Ruud J. G. van Sloun, Rik Vullings

Electrocardiographic signals (ECG) are used in many healthcare applications, including at-home monitoring of vital signs.

Denoising ECG Denoising

Ultrasound Signal Processing: From Models to Deep Learning

no code implementations9 Apr 2022 Ben Luijten, Nishith Chennakeshava, Yonina C. Eldar, Massimo Mischi, Ruud J. G. van Sloun

In this work we provide an overview of these methods from the recent literature, and discuss a wide variety of ultrasound applications.

Deep Task-Based Analog-to-Digital Conversion

1 code implementation29 Jan 2022 Nir Shlezinger, Ariel Amar, Ben Luijten, Ruud J. G. van Sloun, Yonina C. Eldar

In this work we design task-oriented ADCs which learn from data how to map an analog signal into a digital representation such that the system task can be efficiently carried out.

Meta-Learning Quantization

Accelerated Intravascular Ultrasound Imaging using Deep Reinforcement Learning

no code implementations24 Jan 2022 Tristan S. W. Stevens, Nishith Chennakeshava, Frederik J. de Bruijn, Martin Pekař, Ruud J. G. van Sloun

Intravascular ultrasound (IVUS) offers a unique perspective in the treatment of vascular diseases by creating a sequence of ultrasound-slices acquired from within the vessel.

reinforcement-learning reinforcement Learning

Ultrasound Speckle Suppression and Denoising using MRI-derived Normalizing Flow Priors

no code implementations24 Dec 2021 Vincent van de Schaft, Ruud J. G. van Sloun

We here propose a new unsupervised ultrasound speckle reduction and image denoising method based on maximum-a-posteriori estimation with deep generative priors that are learned from high-quality MRI images.

Image Denoising

Deep Proximal Learning for High-Resolution Plane Wave Compounding

no code implementations23 Dec 2021 Nishith Chennakeshava, Ben Luijten, Massimo Mischi, Yonina C. Eldar, Ruud J. G. van Sloun

Plane Wave imaging enables many applications that require high frame rates, including localisation microscopy, shear wave elastography, and ultra-sensitive Doppler.

Inductive Bias

Unsupervised Learned Kalman Filtering

1 code implementation18 Oct 2021 Guy Revach, Nir Shlezinger, Timur Locher, Xiaoyong Ni, Ruud J. G. van Sloun, Yonina C. Eldar

In this paper we adapt KalmanNet, which is a recently pro-posed deep neural network (DNN)-aided system whose architecture follows the operation of the model-based Kalman filter (KF), to learn its mapping in an unsupervised manner, i. e., without requiring ground-truth states.

Uncertainty in Data-Driven Kalman Filtering for Partially Known State-Space Models

1 code implementation10 Oct 2021 Itzik Klein, Guy Revach, Nir Shlezinger, Jonas E. Mehr, Ruud J. G. van Sloun, Yonina. C. Eldar

Providing a metric of uncertainty alongside a state estimate is often crucial when tracking a dynamical system.

RTSNet: Learning to Smooth in Partially Known State-Space Models

2 code implementations10 Oct 2021 Guy Revach, Xiaoyong Ni, Nir Shlezinger, Ruud J. G. van Sloun, Yonina C. Eldar

However, this model-based algorithm is limited in systems that are only partially known, as well as non-linear and non-Gaussian.

A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning

1 code implementation4 Oct 2021 Iris A. M. Huijben, Wouter Kool, Max B. Paulus, Ruud J. G. van Sloun

The Gumbel-max trick is a method to draw a sample from a categorical distribution, given by its unnormalized (log-)probabilities.

BIG-bench Machine Learning

DA-MUSIC: Data-Driven DoA Estimation via Deep Augmented MUSIC Algorithm

2 code implementations22 Sep 2021 Julian P. Merkofer, Guy Revach, Nir Shlezinger, Tirza Routtenberg, Ruud J. G. van Sloun

A popular multi-signal DoA estimation method is the multiple signal classification (MUSIC) algorithm, which enables high-performance super-resolution DoA recovery while being highly applicable in practice.

Super-Resolution

Deep Unfolding with Normalizing Flow Priors for Inverse Problems

no code implementations6 Jul 2021 Xinyi Wei, Hans van Gorp, Lizeth Gonzalez Carabarin, Daniel Freedman, Yonina C. Eldar, Ruud J. G. van Sloun

Many application domains, spanning from computational photography to medical imaging, require recovery of high-fidelity images from noisy, incomplete or partial/compressed measurements.

Deblurring Image Denoising

Dynamic Probabilistic Pruning: A general framework for hardware-constrained pruning at different granularities

no code implementations26 May 2021 Lizeth Gonzalez-Carabarin, Iris A. M. Huijben, Bastiaan S. Veeling, Alexandre Schmid, Ruud J. G. van Sloun

Relevantly, the non-magnitude-based nature of DPP allows for joint optimization of pruning and weight quantization in order to even further compress the network, which we show as well.

Image Classification Network Pruning +1

Deep probabilistic subsampling for task-adaptive compressed sensing

1 code implementation ICLR 2020 Iris A. M. Huijben, Bastiaan S. Veeling, Ruud J. G. van Sloun

The field of deep learning is commonly concerned with optimizing predictive models using large pre-acquired datasets of densely sampled datapoints or signals.

Learning Sampling and Model-Based Signal Recovery for Compressed Sensing MRI

no code implementations22 Apr 2020 Iris A. M. Huijben, Bastiaan S. Veeling, Ruud J. G. van Sloun

Compressed sensing (CS) MRI relies on adequate undersampling of the k-space to accelerate the acquisition without compromising image quality.

Image Reconstruction

Learning Sub-Sampling and Signal Recovery with Applications in Ultrasound Imaging

no code implementations15 Aug 2019 Iris A. M. Huijben, Bastiaan S. Veeling, Kees Janse, Massimo Mischi, Ruud J. G. van Sloun

Compressed sensing emerged as a popular framework for sparse signal reconstruction from a small set of compressed measurements.

Deep Unfolded Robust PCA with Application to Clutter Suppression in Ultrasound

no code implementations20 Nov 2018 Oren Solomon, Regev Cohen, Yi Zhang, Yi Yang, He Qiong, Jianwen Luo, Ruud J. G. van Sloun, Yonina C. Eldar

We compare the performance of the suggested deep network on both simulations and in-vivo rat brain scans, with a commonly practiced deep-network architecture and the fast iterative shrinkage algorithm, and show that our architecture exhibits better image quality and contrast.

Super-Resolution

Super-resolution Ultrasound Localization Microscopy through Deep Learning

no code implementations20 Apr 2018 Ruud J. G. van Sloun, Oren Solomon, Matthew Bruce, Zin Z. Khaing, Hessel Wijkstra, Yonina C. Eldar, Massimo Mischi

This method, which we term Deep Ultrasound Localization Microscopy (Deep-ULM), exploits modern deep learning strategies and employs a convolutional neural network to perform localization microscopy in dense scenarios.

Super-Resolution

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