23 citations · 27 across the 3 of their papers we have counts for
6 papers
Conditional Invertible Neural Networks for Medical Imaging
Alexander Denker, Maximilian Schmidt, Johannes Leuschner +1
Over the last years, deep learning methods have become an increasingly popular choice to solve tasks from the field of inverse problems. Many of these new data-driven methods have…
Conditional Normalizing Flows for Low-Dose Computed Tomography Image Reconstruction
Alexander Denker, Maximilian Schmidt, Johannes Leuschner +2
Image reconstruction from computed tomography (CT) measurement is a challenging statistical inverse problem since a high-dimensional conditional distribution needs to be estimated.…
Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methods
Daniel Otero Baguer, Johannes Leuschner, Maximilian Schmidt
In this work, we investigate the application of deep learning methods for computed tomography in the context of having a low-data regime. As motivation, we review some of the exist…
Deep Relevance Regularization: Interpretable and Robust Tumor Typing of Imaging Mass Spectrometry Data
Christian Etmann, Maximilian Schmidt, Jens Behrmann +6
Neural networks have recently been established as a viable classification method for imaging mass spectrometry data for tumor typing. For multi-laboratory scenarios however, certai…
The LoDoPaB-CT Dataset: A Benchmark Dataset for Low-Dose CT Reconstruction Methods
Johannes Leuschner, Maximilian Schmidt, Daniel Otero Baguer +1
Deep Learning approaches for solving Inverse Problems in imaging have become very effective and are demonstrated to be quite competitive in the field. Comparing these approaches is…
Normalizing flows for novelty detection in industrial time series data
Maximilian Schmidt, Marko Simic
Flow-based deep generative models learn data distributions by transforming a simple base distribution into a complex distribution via a set of invertible transformations. Due to th…