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  2. School of Engineering
  3. Publikationen
  4. Publikationen School of Engineering
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dc.contributor.authorHerzog, Lisa-
dc.contributor.authorMurina, Elvis-
dc.contributor.authorDürr, Oliver-
dc.contributor.authorWegener, Susanne-
dc.contributor.authorSick, Beate-
dc.date.accessioned2024-03-27T12:50:26Z-
dc.date.available2024-03-27T12:50:26Z-
dc.date.issued2020-10-
dc.identifier.issn1361-8415de_CH
dc.identifier.issn1361-8423de_CH
dc.identifier.urihttps://arxiv.org/ftp/arxiv/papers/2008/2008.06332.pdfde_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/30395-
dc.description.abstractAt present, the majority of the proposed Deep Learning (DL) methods provide point predictions without quantifying the model's uncertainty. However, a quantification of the reliability of automated image analysis is essential, in particular in medicine when physicians rely on the results for making critical treatment decisions. In this work, we provide an entire framework to diagnose ischemic stroke patients incorporating Bayesian uncertainty into the analysis procedure. We present a Bayesian Convolutional Neural Network (CNN) yielding a probability for a stroke lesion on 2D Magnetic Resonance (MR) images with corresponding uncertainty information about the reliability of the prediction. For patient-level diagnoses, different aggregation methods are proposed and evaluated, which combine the individual image-level predictions. Those methods take advantage of the uncertainty in the image predictions and report model uncertainty at the patient-level. In a cohort of 511 patients, our Bayesian CNN achieved an accuracy of 95.33% at the image-level representing a significant improvement of 2% over a non-Bayesian counterpart. The best patient aggregation method yielded 95.89% of accuracy. Integrating uncertainty information about image predictions in aggregation models resulted in higher uncertainty measures to false patient classifications, which enabled to filter critical patient diagnoses that are supposed to be closer examined by a medical doctor. We therefore recommend using Bayesian approaches not only for improved image-level prediction and uncertainty estimation but also for the detection of uncertain aggregations at the patient-level.de_CH
dc.language.isoende_CH
dc.publisherElsevierde_CH
dc.relation.ispartofMedical Image Analysisde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectBayesian convolutional neural networksde_CH
dc.subjectIschemic strokede_CH
dc.subjectMagnetic resonance imagingde_CH
dc.subjectUncertaintyde_CH
dc.subjectBayes Theoremde_CH
dc.subjectHumansde_CH
dc.subjectMagnetic Resonance Imagingde_CH
dc.subjectReproducibility of Resultsde_CH
dc.subjectNeural Networks, Computerde_CH
dc.subjectStrokede_CH
dc.subjecteess.IVde_CH
dc.subjectComputer Science - Computer Vision and Pattern Recognitionde_CH
dc.subjectComputer Science - Learningde_CH
dc.subjectQuantitative Biology - Quantitative Methodsde_CH
dc.subjectStatistics - Machine Learningde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleIntegrating uncertainty in deep neural networks for MRI based stroke analysisde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
dc.identifier.doi10.1016/j.media.2020.101790de_CH
dc.identifier.pmid32801096de_CH
zhaw.funding.euNode_CH
zhaw.issue101790de_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume65de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Herzog, L., Murina, E., Dürr, O., Wegener, S., & Sick, B. (2020). Integrating uncertainty in deep neural networks for MRI based stroke analysis. Medical Image Analysis, 65(101790). https://doi.org/10.1016/j.media.2020.101790
Herzog, L. et al. (2020) ‘Integrating uncertainty in deep neural networks for MRI based stroke analysis’, Medical Image Analysis, 65(101790). Available at: https://doi.org/10.1016/j.media.2020.101790.
L. Herzog, E. Murina, O. Dürr, S. Wegener, and B. Sick, “Integrating uncertainty in deep neural networks for MRI based stroke analysis,” Medical Image Analysis, vol. 65, no. 101790, Oct. 2020, doi: 10.1016/j.media.2020.101790.
HERZOG, Lisa, Elvis MURINA, Oliver DÜRR, Susanne WEGENER und Beate SICK, 2020. Integrating uncertainty in deep neural networks for MRI based stroke analysis. Medical Image Analysis [online]. Oktober 2020. Bd. 65, Nr. 101790. DOI 10.1016/j.media.2020.101790. Verfügbar unter: https://arxiv.org/ftp/arxiv/papers/2008/2008.06332.pdf
Herzog, Lisa, Elvis Murina, Oliver Dürr, Susanne Wegener, and Beate Sick. 2020. “Integrating Uncertainty in Deep Neural Networks for MRI Based Stroke Analysis.” Medical Image Analysis 65 (101790). https://doi.org/10.1016/j.media.2020.101790.
Herzog, Lisa, et al. “Integrating Uncertainty in Deep Neural Networks for MRI Based Stroke Analysis.” Medical Image Analysis, vol. 65, no. 101790, Oct. 2020, https://doi.org/10.1016/j.media.2020.101790.


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