6 papers
Structural Limits of the Information-Theoretic Uncertainty Decomposition
Jakob Lønborg Christensen, Christian F. Baumgartner, Morten Rieger Hannemose +2
Uncertainty estimation in machine learning typically decomposes uncertainty into aleatoric uncertainty (AU) and epistemic uncertainty (EU) using the standard information-theoretic…
Rethinking Uncertainty Quantification and Entanglement in Image Segmentation
Jakob Lønborg Christensen, Vedrana Andersen Dahl, Morten Rieger Hannemose +2
Uncertainty quantification (UQ) is crucial in safety-critical applications such as medical image segmentation. Total uncertainty is typically decomposed into data-related aleatoric…
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…
Understanding Benefits and Pitfalls of Current Methods for the Segmentation of Undersampled MRI Data
Jan Nikolas Morshuis, Matthias Hein, Christian F. Baumgartner
MR imaging is a valuable diagnostic tool allowing to non-invasively visualize patient anatomy and pathology with high soft-tissue contrast. However, MRI acquisition is typically ti…
Mind the Detail: Uncovering Clinically Relevant Image Details in Accelerated MRI with Semantically Diverse Reconstructions
Jan Nikolas Morshuis, Christian Schlarmann, Thomas Küstner +2
In recent years, accelerated MRI reconstruction based on deep learning has led to significant improvements in image quality with impressive results for high acceleration factors. H…
Segmentation-guided MRI reconstruction for meaningfully diverse reconstructions
Jan Nikolas Morshuis, Matthias Hein, Christian F. Baumgartner
Inverse problems, such as accelerated MRI reconstruction, are ill-posed and an infinite amount of possible and plausible solutions exist. This may not only lead to uncertainty in t…