Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation

• URL: http://arxiv.org/abs/2104.13756v1
• Date: Wed, 28 Apr 2021 13:37:10 GMT
• Title: Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation
• Authors: Sebastian G. Popescu, David J. Sharp, James H. Cole, Konstantinos Kamnitsas, Ben Glocker
• Abstract summary: We propose a parameter efficient Bayesian layer for hierarchical convolutional Gaussian Processes. Our experiments on brain tissue-segmentation show that the resulting architecture approaches the performance of well-established deterministic segmentation algorithms. Our uncertainty estimates result in out-of-distribution detection that outperforms the capabilities of previous Bayesian networks.
• Score: 15.086527565572073
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a parameter efficient Bayesian layer for hierarchical convolutional Gaussian Processes that incorporates Gaussian Processes operating in Wasserstein-2 space to reliably propagate uncertainty. This directly replaces convolving Gaussian Processes with a distance-preserving affine operator on distributions. Our experiments on brain tissue-segmentation show that the resulting architecture approaches the performance of well-established deterministic segmentation algorithms (U-Net), which has never been achieved with previous hierarchical Gaussian Processes. Moreover, by applying the same segmentation model to out-of-distribution data (i.e., images with pathology such as brain tumors), we show that our uncertainty estimates result in out-of-distribution detection that outperforms the capabilities of previous Bayesian networks and reconstruction-based approaches that learn normative distributions.

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