1 citations · 1 across the 6 of their papers we have counts for
8 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…
Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm
Christian Møller Mikkelstrup, Anders Bjorholm Dahl, Philip Bille +2
Computing a minimum - cut in a graph is a solution to a wide range of computer vision problems and is often done using the Boykov-Kolmogorov (BK) algorithm. In this paper, we…
FaCT-GS: Fast and Scalable CT Reconstruction with Gaussian Splatting
Pawel Tomasz Pieta, Rasmus Juul Pedersen, Sina Borgi +3
Gaussian Splatting (GS) has emerged as a dominating technique for image rendering and has quickly been adapted for the X-ray Computed Tomography (CT) reconstruction task. However,…
VoDaSuRe: A Large-Scale Dataset Revealing Domain Shift in Volumetric Super-Resolution
August Leander Høeg, Sophia Wiinberg Bardenfleth, Hans Martin Kjer +3
Recent advances in volumetric super-resolution (SR) have demonstrated strong performance in medical and scientific imaging, with transformer- and CNN-based approaches achieving imp…
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…
MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range Interactions
August Leander Høeg, Sophia W. Bardenfleth, Hans Martin Kjer +3
Until now, it has been difficult for volumetric super-resolution to utilize the recent advances in transformer-based models seen in 2D super-resolution. The memory required for sel…