Laplacian Segmentation Networks Improve Epistemic Uncertainty Quantification

Understand

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.