no code implementations • 28 Mar 2023 • Kilian Zepf, Eike Petersen, Jes Frellsen, Aasa Feragen
Segmentation uncertainty models predict a distribution over plausible segmentations for a given input, which they learn from the annotator variation in the training set.
no code implementations • 23 Mar 2023 • Kilian Zepf, Selma Wanna, Marco Miani, Juston Moore, Jes Frellsen, Søren Hauberg, Aasa Feragen, Frederik Warburg
To ensure robustness to such incorrect segmentations, we propose Laplacian Segmentation Networks (LSN) that jointly model epistemic (model) and aleatoric (data) uncertainty in image segmentation.
no code implementations • 20 May 2021 • Kasra Arnavaz, Oswin Krause, Kilian Zepf, Jelena M. Krivokapic, Silja Heilmann, Jakob Andreas Bærentzen, Pia Nyeng, Aasa Feragen
b) We provide a full deep-learning methodology for this difficult noisy task on time-series image data.