32 citations · 58 across the 7 of their papers we have counts for
7 papers · 1 filter
Deeply supervised UNet for semantic segmentation to assist dermatopathological assessment of Basal Cell Carcinoma (BCC)
Jean Le'Clerc Arrastia, Nick Heilenkötter, Daniel Otero Baguer +6
Accurate and fast assessment of resection margins is an essential part of a dermatopathologist's clinical routine. In this work, we successfully develop a deep learning method to a…
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…
A Projectional Ansatz to Reconstruction
Sören Dittmer, Peter Maass
Recently the field of inverse problems has seen a growing usage of mathematically only partially understood learned and non-learned priors. Based on first principles, we develop a…
Regularization by architecture: A deep prior approach for inverse problems
Sören Dittmer, Tobias Kluth, Peter Maass +1
The present paper studies so-called deep image prior (DIP) techniques in the context of ill-posed inverse problems. DIP networks have been recently introduced for applications in i…
Singular Values for ReLU Layers
Sören Dittmer, Emily J. King, Peter Maass
Despite their prevalence in neural networks we still lack a thorough theoretical characterization of ReLU layers. This paper aims to further our understanding of ReLU layers by stu…
A Survey on Surrogate Approaches to Non-negative Matrix Factorization
Pascal Fernsel, Peter Maass
Motivated by applications in hyperspectral imaging we investigate methods for approximating a high-dimensional non-negative matrix by a product of two lower-d…