376 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of BonnDE7 papers
- Athinoula A. Martinos Center for Biomedical ImagingUS3 papers
- German Center for Neurodegenerative DiseasesDE3 papers
- Harvard UniversityUS3 papers
- Massachusetts General HospitalUS3 papers
- Friedrich-Alexander-Universität Erlangen-NürnbergDE2 papers
- Johannes Gutenberg University MainzDE2 papers
- Alexander von Humboldt FoundationDE1 paper
- Center for Mathematical ModelingCL1 paper
- Centre National de la Recherche ScientifiqueFR1 paper
- German Centre for Cardiovascular ResearchDE1 paper
- Heidelberg UniversityDE1 paper
9 papers
FastSurfer-HypVINN: Automated sub-segmentation of the hypothalamus and adjacent structures on high-resolutional brain MRI
Santiago Estrada, David Kügler, Emad Bahrami +5
The hypothalamus plays a crucial role in the regulation of a broad range of physiological, behavioural, and cognitive functions. However, despite its importance, only a few small-s…
Cover and Hitting Times of Hyperbolic Random Graphs
Marcos Kiwi, Markus Schepers, John Sylvester
We study random walks on the giant component of Hyperbolic Random Graphs (HRGs), in the regime when the degree distribution obeys a power law with exponent in the range . In…
Automated Olfactory Bulb Segmentation on High Resolutional T2-Weighted MRI
Santiago Estrada, Ran Lu, Kersten Diers +5
The neuroimage analysis community has neglected the automated segmentation of the olfactory bulb (OB) despite its crucial role in olfactory function. The lack of an automatic proce…
FatSegNet : A Fully Automated Deep Learning Pipeline for Adipose Tissue Segmentation on Abdominal Dixon MRI
Santiago Estrada, Ran Lu, Sailesh Conjeti +4
Purpose: Development of a fast and fully automated deep learning pipeline (FatSegNet) to accurately identify, segment, and quantify abdominal adipose tissue on Dixon MRI from the R…
Coupled Continuous Time Random Maxima
Katharina Hees, Hans-Peter Scheffler
Continuous Time Random Maxima (CTRM) are a generalization of classical extreme value theory: Instead of observing random events at regular intervals in time, the waiting times betw…
On the use of Harrell's C for clinical risk prediction via random survival forests
Matthias Schmid, Marvin Wright, Andreas Ziegler
Random survival forests (RSF) are a powerful method for risk prediction of right-censored outcomes in biomedical research. RSF use the log-rank split criterion to form an ensemble…
The Complexity of Finding Effectors
Laurent Bulteau, Stefan Fafianie, Vincent Froese +2
The NP-hard EFFECTORS problem on directed graphs is motivated by applications in network mining, particularly concerning the analysis of probabilistic information-propagation proce…
Extending Statistical Boosting - An Overview of Recent Methodological Developments
Andreas Mayr, Harald Binder, Olaf Gefeller +1
Boosting algorithms to simultaneously estimate and select predictor effects in statistical models have gained substantial interest during the last decade. This review article aims…
The Evolution of Boosting Algorithms - From Machine Learning to Statistical Modelling
Andreas Mayr, Harald Binder, Olaf Gefeller +1
The concept of boosting emerged from the field of machine learning. The basic idea is to boost the accuracy of a weak classifying tool by combining various instances into a more ac…