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Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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)
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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Publikationstyp
Artikel: Konferenzbeitrag
Schlagwörter
Tasks
ISSN (print) / ISBN
0302-9743
e-ISSN
1611-3349
Konferenztitel
5th International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (UNSURE)
Konferzenzdatum
12 October 2023
Konferenzort
Vancouver, CANADA
Zeitschrift
Lecture Notes in Computer Science
Quellenangaben
Band: 14291,
Seiten: 1-11
Verlag
Springer
Verlagsort
Berlin [u.a.]
Nichtpatentliteratur
Publikationen
Institut(e)
Institute for Machine Learning in Biomed Imaging (IML)