147 citations · 234 across the 13 of their papers we have counts for
14 papers
2-Step Sparse-View CT Reconstruction with a Domain-Specific Perceptual Network
Haoyu Wei, Florian Schiffers, Tobias Würfl +4
Computed tomography is widely used to examine internal structures in a non-destructive manner. To obtain high-quality reconstructions, one typically has to acquire a densely sample…
Disassemblable Fieldwork CT Scanner Using a 3D-printed Calibration Phantom
Florian Schiffers, Thomas Bochynek, Andre Aichert +3
The use of computed tomography (CT) imaging has become of increasing interest to academic areas outside of the field of medical imaging and industrial inspection, e.g., to biology…
Reconstruction of Voxels with Position- and Angle-Dependent Weightings
Lina Felsner, Tobias Würfl, Christopher Syben +4
The reconstruction problem of voxels with individual weightings can be modeled a position- and angle- dependent function in the forward-projection. This changes the system matrix a…
Projection-to-Projection Translation for Hybrid X-ray and Magnetic Resonance Imaging
Bernhard Stimpel, Christopher Syben, Tobias Würfl +4
Hybrid X-ray and magnetic resonance (MR) imaging promises large potential in interventional medical imaging applications due to the broad variety of contrast of MRI combined with f…
The TCGA Meta-Dataset Clinical Benchmark
Mandana Samiei, Tobias Würfl, Tristan Deleu +6
Machine learning is bringing a paradigm shift to healthcare by changing the process of disease diagnosis and prognosis in clinics and hospitals. This development equips doctors and…
Torchmeta: A Meta-Learning library for PyTorch
Tristan Deleu, Tobias Würfl, Mandana Samiei +2
The constant introduction of standardized benchmarks in the literature has helped accelerating the recent advances in meta-learning research. They offer a way to get a fair compari…
Learning with Known Operators reduces Maximum Training Error Bounds
Andreas K. Maier, Christopher Syben, Bernhard Stimpel +7
We describe an approach for incorporating prior knowledge into machine learning algorithms. We aim at applications in physics and signal processing in which we know that certain op…
Deriving Neural Network Architectures using Precision Learning: Parallel-to-fan beam Conversion
Christopher Syben, Bernhard Stimpel, Jonathan Lommen +3
In this paper, we derive a neural network architecture based on an analytical formulation of the parallel-to-fan beam conversion problem following the concept of precision learning…
Towards Arbitrary Noise Augmentation - Deep Learning for Sampling from Arbitrary Probability Distributions
Felix Horger, Tobias Würfl, Vincent Christlein +1
Accurate noise modelling is important for training of deep learning reconstruction algorithms. While noise models are well known for traditional imaging techniques, the noise distr…
Precision Learning: Towards Use of Known Operators in Neural Networks
Andreas Maier, Frank Schebesch, Christopher Syben +4
In this paper, we consider the use of prior knowledge within neural networks. In particular, we investigate the effect of a known transform within the mapping from input data space…
Frangi-Net: A Neural Network Approach to Vessel Segmentation
Weilin Fu, Katharina Breininger, Tobias Würfl +3
In this paper, we reformulate the conventional 2-D Frangi vesselness measure into a pre-weighted neural network ("Frangi-Net"), and illustrate that the Frangi-Net is equivalent to…
MR to X-Ray Projection Image Synthesis
Bernhard Stimpel, Christopher Syben, Tobias Würfl +3
Hybrid imaging promises large potential in medical imaging applications. To fully utilize the possibilities of corresponding information from different modalities, the information…
Precision Learning: Reconstruction Filter Kernel Discretization
Christopher Syben, Bernhard Stimpel, Katharina Breininger +4
In this paper, we present substantial evidence that a deep neural network will intrinsically learn the appropriate way to discretize the ideal continuous reconstruction filter. Cur…
Patch-based Carcinoma Detection on Confocal Laser Endomicroscopy Images -- A Cross-Site Robustness Assessment
Marc Aubreville, Miguel Goncalves, Christian Knipfer +6
Deep learning technologies such as convolutional neural networks (CNN) provide powerful methods for image recognition and have recently been employed in the field of automated carc…