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.

翻译：图像分割严重依赖于神经网络，而神经网络往往表现出过度自信，尤其是在对分布外（OOD）图像进行预测时。由于设备、采集站点或图像损坏的差异，这在医学领域是一个常见场景。本研究通过提出拉普拉斯分割网络（LSN）来解决OOD检测的挑战：这些方法联合建模认知（模型）不确定性和偶然（数据）不确定性以进行OOD检测。在此过程中，我们提出了首个适用于具有高维输出跳跃连接的大型神经网络的权重后验拉普拉斯近似。我们在三个数据集上证明，LSN建模的参数分布结合合适的不确定性度量，能够实现卓越的OOD检测。