Popescu, SG;
Sharp, DJ;
Cole, JH;
Kamnitsas, K;
Glocker, B;
(2021)
Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation.
In:
Information Processing in Medical Imaging.
(pp. pp. 415-427).
Springer: Cham, Switzerland.
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation |
| ISBN-13: | 9783030781903 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-030-78191-0_32 |
| Publisher version: | https://doi.org/10.1007/978-3-030-78191-0_32 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10134583 |
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