Laplacian Segmentation Networks Improve Epistemic Uncertainty Quantification

• URL: http://arxiv.org/abs/2303.13123v2
• Date: Tue, 23 Jul 2024 14:38:34 GMT
• Title: Laplacian Segmentation Networks Improve Epistemic Uncertainty Quantification
• Authors: Kilian Zepf, Selma Wanna, Marco Miani, Juston Moore, Jes Frellsen, Søren Hauberg, Frederik Warburg, Aasa Feragen,
• Abstract summary: Image segmentation relies heavily on neural networks which are known to be overconfident. This is a common scenario in the medical domain due to variations in equipment, acquisition sites, or image corruptions. We propose Laplacian Networks (LSN): methods which jointly model (model) and aleatoric (data) uncertainty for OOD detection.
• Score: 21.154979285736268
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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