Author

Paul Fischer

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Preprint Aug 2026

VIDS-Seg: Towards Reliable Uncertainty Quantification in Pediatric Cardiac Ultrasound Segmentation

Reliable clinical deployment of machine learning requires models that know when they are likely to fail, particularly for subgroups underrepresented in training data. A common case is pediatric care, where models trained on adult cohorts can silently under-perform on children with no indication that something has gone...

Paul Fischer, Ece Ozkan · 0 citations

Open access Aug 2026

Uncertainty estimation for reliable neural network-based radiotherapy dose modelling using Monte Carlo dropout, mean variance estimation and deep ensemble

Background and Purpose Neural networks promise fast dose modelling with high accuracy for challenging situations like magnetic resonance imaging (MRI)-guided radiotherapy. As they are data-driven, failure can occur and early identification of erroneous dose calculations is required. In this study, we implemented and ev...

Moritz Schneider, T. Eberhardt, C. Gani et al. · Physics and Imaging in Radia... · 0 citations