18 citations · 28 across the 5 of their papers we have counts for
5 papers
Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game
Sebastian Niehaus, Ingo Roeder, Nico Scherf
Decentralised learning enables the training of deep learning algorithms without centralising data sets, resulting in benefits such as improved data privacy, operational efficiency…
Automatic quality control framework for more reliable integration of machine learning-based image segmentation into medical workflows
Elena Williams, Sebastian Niehaus, Janis Reinelt +9
Machine learning algorithms underpin modern diagnostic-aiding software, which has proved valuable in clinical practice, particularly in radiology. However, inaccuracies, mainly due…
A comparative study of semi- and self-supervised semantic segmentation of biomedical microscopy data
Nastassya Horlava, Alisa Mironenko, Sebastian Niehaus +3
In recent years, Convolutional Neural Networks (CNNs) have become the state-of-the-art method for biomedical image analysis. However, these networks are usually trained in a superv…
Domain specific cues improve robustness of deep learning based segmentation of ct volumes
Marie Kloenne, Sebastian Niehaus, Leonie Lampe +4
Machine Learning has considerably improved medical image analysis in the past years. Although data-driven approaches are intrinsically adaptive and thus, generic, they often do not…
Multi-Stage Reinforcement Learning For Object Detection
Jonas Koenig, Simon Malberg, Martin Martens +3
We present a reinforcement learning approach for detecting objects within an image. Our approach performs a step-wise deformation of a bounding box with the goal of tightly framing…