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Feiner, L.F.* ; Menten, M.J.* ; Hammernik, K.* ; Hager, P.* ; Huang, W.* ; Rueckert, D.* ; Braren, R.F.* ; Kaissis, G.

Propagation and attribution of uncertainty in medical imaging pipelines.

In: (5th International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (UNSURE), 12 October 2023, Vancouver, CANADA). Berlin [u.a.]: Springer, 2023. 1-11 (Lect. Notes Comput. Sc. ; 14291)
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Open Access Green as soon as Postprint is submitted to ZB.
Uncertainty estimation, which provides a means of building explainable neural networks for medical imaging applications, have mostly been studied for single deep learning models that focus on a specific task. In this paper, we propose a method to propagate uncertainty through cascades of deep learning models in medical imaging pipelines. This allows us to aggregate the uncertainty in later stages of the pipeline and to obtain a joint uncertainty measure for the predictions of later models. Additionally, we can separately report contributions of the aleatoric, data-based, uncertainty of every component in the pipeline. We demonstrate the utility of our method on a realistic imaging pipeline that reconstructs undersampled brain and knee magnetic resonance (MR) images and subsequently predicts quantitative information from the images, such as the brain volume, or knee side or patient's sex. We quantitatively show that the propagated uncertainty is correlated with input uncertainty and compare the proportions of contributions of pipeline stages to the joint uncertainty measure.
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Publication type Article: Conference contribution
Keywords Tasks
Language english
Publication Year 2023
HGF-reported in Year 2023
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Conference Title 5th International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (UNSURE)
Conference Date 12 October 2023
Conference Location Vancouver, CANADA
Journal Lecture Notes in Computer Science
Quellenangaben Volume: 14291, Issue: , Pages: 1-11 Article Number: , Supplement: ,
Publisher Springer
Publishing Place Berlin [u.a.]
Institute(s) Institute for Machine Learning in Biomed Imaging (IML)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-507100-001
DOI 10.1007/978-3-031-44336-7_1
WOS ID 001115884500001
Erfassungsdatum 2024-01-18
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