87 citations · 253 across the 8 of their papers we have counts for
9 papers
Convolutional Dictionary Learning by End-To-End Training of Iterative Neural Networks
Andreas Kofler, Christian Wald, Tobias Schaeffter +2
Sparsity-based methods have a long history in the field of signal processing and have been successfully applied to various image reconstruction problems. The involved sparsifying t…
Convolutional Analysis Operator Learning by End-To-End Training of Iterative Neural Networks
Andreas Kofler, Christian Wald, Tobias Schaeffter +2
The concept of sparsity has been extensively applied for regularization in image reconstruction. Typically, sparsifying transforms are either pre-trained on ground-truth images or…
An End-To-End-Trainable Iterative Network Architecture for Accelerated Radial Multi-Coil 2D Cine MR Image Reconstruction
Andreas Kofler, Markus Haltmeier, Tobias Schaeffter +1
Purpose: Iterative Convolutional Neural Networks (CNNs) which resemble unrolled learned iterative schemes have shown to consistently deliver state-of-the-art results for image reco…
Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL
Nils Strodthoff, Patrick Wagner, Tobias Schaeffter +1
Electrocardiography is a very common, non-invasive diagnostic procedure and its interpretation is increasingly supported by automatic interpretation algorithms. The progress in the…
Unsupervised Adaptive Neural Network Regularization for Accelerated Radial Cine MRI
Andreas Kofler, Marc Dewey, Tobias Schaeffter +2
In this work, we propose an iterative reconstruction scheme (ALONE - Adaptive Learning Of NEtworks) for 2D radial cine MRI based on ground truth-free unsupervised learning of shall…
Neural Networks-based Regularization for Large-Scale Medical Image Reconstruction
Andreas Kofler, Markus Haltmeier, Tobias Schaeffter +4
In this paper we present a generalized Deep Learning-based approach for solving ill-posed large-scale inverse problems occuring in medical image reconstruction. Recently, Deep Lear…