2 papers
cs.CV2026
VIDS-Seg: Towards Reliable Uncertainty Quantification in Pediatric Cardiac Ultrasound Segmentation
Paul Fischer, Ece Ozkan
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…
eess.IV2025
CUTE-MRI: Conformalized Uncertainty-based framework for Time-adaptivE MRI
Paul Fischer, Jan Nikolas Morshuis, Thomas Küstner +1
Magnetic Resonance Imaging (MRI) offers unparalleled soft-tissue contrast but is fundamentally limited by long acquisition times. While deep learning-based accelerated MRI can dram…