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2021 | OriginalPaper | Chapter

Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation

Authors : Sebastian G. Popescu, David J. Sharp, James H. Cole, Konstantinos Kamnitsas, Ben Glocker

Published in: Information Processing in Medical Imaging

Publisher: Springer International Publishing

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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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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
Publisher: Springer International Publishing
Book: Information Processing in Medical Imaging
Print ISBN: 978-3-030-78190-3

Electronic ISBN: 978-3-030-78191-0

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-78191-0_32

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