bibtext: @InProceedings{ Zep_Laplacian_MICCAI2024,
                   author = { Zepf, Kilian and Wanna, Selma and Miani, Marco and Moore, Juston and Frellsen, Jes and Hauberg, Søren and Warburg, Frederik and Feragen, Aasa },
                   title = { { Laplacian Segmentation Networks Improve Epistemic Uncertainty Quantification } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15008 },
                   month = {October},
                   pages = { pending },
                }
abstract: Image segmentation relies heavily on neural networks which are known to be overconfident, especially when making predictions on out-of-distribution (OOD) images. This is a common scenario in the medical domain due to variations in equipment, acquisition sites, or image corruptions. This work addresses the challenge of OOD detection by proposing Laplacian Segmentation Networks (LSN): methods which jointly model epistemic (model) and aleatoric (data) uncertainty for OOD detection. In doing so, we propose the first Laplace approximation of the weight posterior that scales to large neural networks with skip connections that have high-dimensional outputs. We demonstrate on three datasets that the LSN-modeled parameter distributions, in combination with suitable uncertainty measures, gives superior OOD detection.
title: Laplacian Segmentation Networks Improve Epistemic Uncertainty Quantification
authors: Zepf, Kilian Wanna, Selma Miani, Marco Moore, Juston Frellsen, Jes Hauberg, Søren Warburg, Frederik Feragen, Aasa