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Dominik Narnhofer
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2020 – today
- 2024
- [j2]Dominik Narnhofer, Andreas Habring, Martin Holler, Thomas Pock:
Posterior-Variance-Based Error Quantification for Inverse Problems in Imaging. SIAM J. Imaging Sci. 17(1): 301-333 (2024) - [i7]Jakub Micorek, Horst Possegger, Dominik Narnhofer, Horst Bischof, Mateusz Kozinski:
MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection. CoRR abs/2403.14497 (2024) - [i6]Michelle Halbheer, Dominik J. Mühlematter, Alexander Becker, Dominik Narnhofer, Helge Aasen, Konrad Schindler, Mehmet Ozgur Turkoglu:
LoRA-Ensemble: Efficient Uncertainty Modelling for Self-attention Networks. CoRR abs/2405.14438 (2024) - [i5]Lea Bogensperger, Dominik Narnhofer, Alexander Falk, Konrad Schindler, Thomas Pock:
FlowSDF: Flow Matching for Medical Image Segmentation Using Distance Transforms. CoRR abs/2405.18087 (2024) - 2023
- [c2]Lea Bogensperger, Dominik Narnhofer, Filip Ilic, Thomas Pock:
Score-Based Generative Models for Medical Image Segmentation Using Signed Distance Functions. DAGM 2023: 3-17 - [i4]Lea Bogensperger, Dominik Narnhofer, Filip Ilic, Thomas Pock:
Score-Based Generative Models for Medical Image Segmentation using Signed Distance Functions. CoRR abs/2303.05966 (2023) - [i3]Alessandro Benfenati, Emilie Chouzenoux, Giorgia Franchini, Salla Latva-Aijo, Dominik Narnhofer, Jean-Christophe Pesquet, Sebastian J. Scott, Mahsa Yousefi:
Majorization-Minimization for sparse SVMs. CoRR abs/2308.16858 (2023) - 2022
- [j1]Dominik Narnhofer, Alexander Effland, Erich Kobler, Kerstin Hammernik, Florian Knoll, Thomas Pock:
Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction. IEEE Trans. Medical Imaging 41(2): 279-291 (2022) - [i2]Dominik Narnhofer, Andreas Habring, Martin Holler, Thomas Pock:
Posterior-Variance-Based Error Quantification for Inverse Problems in Imaging. CoRR abs/2212.12499 (2022) - 2021
- [i1]Dominik Narnhofer, Alexander Effland, Erich Kobler, Kerstin Hammernik, Florian Knoll, Thomas Pock:
Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction. CoRR abs/2102.06665 (2021)
2010 – 2019
- 2019
- [c1]Lydia Lindner, Dominik Narnhofer, Maximilian Weber, Christina Gsaxner, Malgorzata Kolodziej, Jan Egger:
Using Synthetic Training Data for Deep Learning-Based GBM Segmentation. EMBC 2019: 6724-6729
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