1 code implementation • 1 Jun 2021 • Martin Genzel, Jan Macdonald, Maximilian März
This report is dedicated to a short motivation and description of our contribution to the AAPM DL-Sparse-View CT Challenge (team name: "robust-and-stable").
1 code implementation • 14 Jun 2022 • Martin Genzel, Ingo Gühring, Jan Macdonald, Maximilian März
This work is concerned with the following fundamental question in scientific machine learning: Can deep-learning-based methods solve noise-free inverse problems to near-perfect accuracy?
1 code implementation • 9 Nov 2020 • Martin Genzel, Jan Macdonald, Maximilian März
In the past five years, deep learning methods have become state-of-the-art in solving various inverse problems.
1 code implementation • 18 Nov 2022 • Theophil Trippe, Martin Genzel, Jan Macdonald, Maximilian März
This work presents a novel deep-learning-based pipeline for the inverse problem of image deblurring, leveraging augmentation and pre-training with synthetic data.
1 code implementation • 25 Mar 2020 • Luis Oala, Cosmas Heiß, Jan Macdonald, Maximilian März, Wojciech Samek, Gitta Kutyniok
We propose a fast, non-Bayesian method for producing uncertainty scores in the output of pre-trained deep neural networks (DNNs) using a data-driven interval propagating network.
1 code implementation • 27 Mar 2020 • Jan Macdonald, Maximilian März, Luis Oala, Wojciech Samek
This work investigates the detection of instabilities that may occur when utilizing deep learning models for image reconstruction tasks.
no code implementations • 1 May 2017 • Jackie Ma, Maximilian März, Stephanie Funk, Jeanette Schulz-Menger, Gitta Kutyniok, Tobias Schaeffter, Christoph Kolbitsch
High-resolution three-dimensional (3D) cardiovascular magnetic resonance (CMR) is a valuable medical imaging technique, but its widespread application in clinical practice is hampered by long acquisition times.
no code implementations • NeurIPS Workshop Deep_Invers 2021 • Martin Genzel, Ingo Gühring, Jan Macdonald, Maximilian März
This work presents an empirical study on the design and training of iterative neural networks for image reconstruction from tomographic measurements with unknown geometry.