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Out of distribution (OOD) medical images are frequently encountered, e.g.because of site- or scanner differences, or image corruption. OOD images comewith a risk of incorrect image segmentation, potentially negatively affectingdownstream diagnoses or treatment. To ensure robustness to such incorrectsegmentations, we propose Laplacian Segmentation Networks (LSN) that jointlymodel epistemic (model) and aleatoric (data) uncertainty in image segmentation.We capture data uncertainty with a spatially correlated logit distribution. Formodel uncertainty, we propose the first Laplace approximation of the weightposterior that scales to large neural networks with skip connections that havehigh-dimensional outputs. Empirically, we demonstrate that modelling spatialpixel correlation allows the Laplacian Segmentation Network to successfullyassign high epistemic uncertainty to out-of-distribution objects appearingwithin images.

Laplacian Segmentation Networks: Improved Epistemic Uncertainty from Spatial Aleatoric Uncertainty