179 citations · 338 across the 9 of their papers we have counts for
3 papers · 1 filter
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…