1 citations · 1 across the 6 of their papers we have counts for
6 papers
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…
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…
Conformal Performance Range Prediction for Segmentation Output Quality Control
Anna M. Wundram, Paul Fischer, Michael Muehlebach +2
Recent works have introduced methods to estimate segmentation performance without ground truth, relying solely on neural network softmax outputs. These techniques hold potential fo…
PULPo: Probabilistic Unsupervised Laplacian Pyramid Registration
Leonard Siegert, Paul Fischer, Mattias P. Heinrich +1
Deformable image registration is fundamental to many medical imaging applications. Registration is an inherently ambiguous task often admitting many viable solutions. While neural…
Subgroup-Specific Risk-Controlled Dose Estimation in Radiotherapy
Paul Fischer, Hannah Willms, Moritz Schneider +3
Cancer remains a leading cause of death, highlighting the importance of effective radiotherapy (RT). Magnetic resonance-guided linear accelerators (MR-Linacs) enable imaging during…
Uncertainty Estimation and Propagation in Accelerated MRI Reconstruction
Paul Fischer, Thomas Küstner, Christian F. Baumgartner
MRI reconstruction techniques based on deep learning have led to unprecedented reconstruction quality especially in highly accelerated settings. However, deep learning techniques a…