3.5k citations
- Santa Fe InstituteUS79 papers
- Complexity Science HubAT54 papers
- International Institute for Applied Systems AnalysisAT38 papers
- University of ViennaAT25 papers
- Austrian Research Institute for Artificial IntelligenceAT19 papers
- Imperial College LondonGB15 papers
- University College LondonGB15 papers
- Czech Technical University in PragueCZ13 papers
- Statistics AustriaAT13 papers
- King's College LondonGB12 papers
- University of OxfordGB12 papers
- Technical University of MunichDE11 papers
318 papers
SYNPRED: A Synergistic Approach to Multimodal Learning for Clinical Prediction
Ivana Janíčková, Yen Y. Tan, Thomas H. Helbich +6
Multimodal medical data captures complementary aspects of disease. It provides a comprehensive basis for diagnosis and the identification of biological associations linked to progr…
Bridging the gap: Using deep learning to reconstruct noise-reduced super-resolved OCT images from gapped spectra
Jonas Nienhaus, Thomas Schlegl, Wolfgang Drexler +2
Fourier-domain (FD) optical coherence tomography (OCT) depends on broadband sources to maximize axial resolution and image quality. However, these lasers significantly drive device…
Towards patient-specific optimization for mandibular reconstruction planning based on predicted bone-union propensity
Hamidreza Aftabi, John E. Lloyd, Amanda Ding +4
Mandibular reconstruction with vascularized bone grafts is complicated by donor-host nonunion, and virtual surgical planning produces a geometric plan rather than optimizing for bo…
Overcoming Barriers to Computational Reproducibility
Roman Hornung, László Németh, Oleksandr Zadorozhny +13
Computational reproducibility, the possibility for independent researchers to exactly reproduce published empirical results, is fundamental to science. Despite its importance, the…
MRI-derived quantification of hepatic vessel-to-volume ratios in chronic liver disease using a deep learning approach
Alexander Herold, Daniel Sobotka, Lucian Beer +12
Background: We aimed to quantify hepatic vessel volumes across chronic liver disease stages and healthy controls using deep learning-based magnetic resonance imaging (MRI) analysis…
Empirical validation of the polarization transition in a double-random field model of elections
Jan Korbel, Remah Dahdoul, Stefan Thurner
We model bipartisan elections where voters are exposed to two forces: local homophilic interactions and external influence from two political campaigns. The model is mathematically…