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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Acknowledgments
This work was supported by VILLUM FONDEN (grants 15334, 42062), the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant 757360), Novo Nordisk Foundation (NNF20O-C0062606), LANL (LA-UR-24-23937) LDRD grant 20210043DR (U.S. DOE NNSA Contract 89233218CNA000001), and the Pioneer Centre for AI (DNRF grant P1).
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Zepf, K. et al. (2024). Laplacian Segmentation Networks Improve Epistemic Uncertainty Quantification. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15008. Springer, Cham. https://doi.org/10.1007/978-3-031-72111-3_33
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