no code implementations • 12 Feb 2021 • Dominik Narnhofer, Alexander Effland, Erich Kobler, Kerstin Hammernik, Florian Knoll, Thomas Pock
To this end, we solve the linear inverse problem of undersampled MRI reconstruction in a variational setting.
no code implementations • 23 Dec 2022 • Dominik Narnhofer, Andreas Habring, Martin Holler, Thomas Pock
The proposed method employs estimates of the posterior variance together with techniques from conformal prediction in order to obtain coverage guarantees for the error bounds, without making any assumption on the underlying data distribution.
no code implementations • 10 Mar 2023 • Lea Bogensperger, Dominik Narnhofer, Filip Ilic, Thomas Pock
Medical image segmentation is a crucial task that relies on the ability to accurately identify and isolate regions of interest in medical images.
no code implementations • 31 Aug 2023 • Alessandro Benfenati, Emilie Chouzenoux, Giorgia Franchini, Salla Latva-Aijo, Dominik Narnhofer, Jean-Christophe Pesquet, Sebastian J. Scott, Mahsa Yousefi
Several decades ago, Support Vector Machines (SVMs) were introduced for performing binary classification tasks, under a supervised framework.
no code implementations • 21 Mar 2024 • Jakub Micorek, Horst Possegger, Dominik Narnhofer, Horst Bischof, Mateusz Kozinski
We propose a novel approach to video anomaly detection: we treat feature vectors extracted from videos as realizations of a random variable with a fixed distribution and model this distribution with a neural network.