1 citations · 1 across the 3 of their papers we have counts for
4 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…
Towards Agnostic and Holistic Universal Image Segmentation with Bit Diffusion
Jakob Lønborg Christensen, Morten Rieger Hannemose, Anders Bjorholm Dahl +1
This paper introduces a diffusion-based framework for universal image segmentation, making agnostic segmentation possible without depending on mask-based frameworks and instead pre…
Diffusion Based Ambiguous Image Segmentation
Jakob Lønborg Christensen, Morten Rieger Hannemose, Anders Bjorholm Dahl +1
Medical image segmentation often involves inherent uncertainty due to variations in expert annotations. Capturing this uncertainty is an important goal and previous works have used…