eprintid: 10134583
rev_number: 17
eprint_status: archive
userid: 608
dir: disk0/10/13/45/83
datestamp: 2021-09-17 11:46:08
lastmod: 2022-06-15 06:10:54
status_changed: 2021-09-17 11:46:08
type: proceedings_section
metadata_visibility: show
creators_name: Popescu, SG
creators_name: Sharp, DJ
creators_name: Cole, JH
creators_name: Kamnitsas, K
creators_name: Glocker, B
title: Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
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.
date: 2021-01-01
date_type: published
publisher: Springer
official_url: https://doi.org/10.1007/978-3-030-78191-0_32
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1881977
doi: 10.1007/978-3-030-78191-0_32
isbn_13: 9783030781903
lyricists_name: Cole, James
lyricists_id: JCOLE07
actors_name: Cole, James
actors_id: JCOLE07
actors_role: owner
full_text_status: public
series: Lecture Notes in Computer Science
publication: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
volume: 12729
place_of_pub: Cham, Switzerland
pagerange: 415-427
issn: 1611-3349
book_title: Information Processing in Medical Imaging
citation:        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.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10134583/1/Distributional_Gaussian_Process_Layers_for_Outlier_Detection_in_Image_Segmentation____IPMI_version.pdf