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dc.contributor.authorStorås, Andrea
dc.contributor.authorAndersen, Ole Emil
dc.contributor.authorLockhart, Sam
dc.contributor.authorThielemann, Roman
dc.contributor.authorGnesin, Filip
dc.contributor.authorThambawita, Vajira L B
dc.contributor.authorHicks, Steven
dc.contributor.authorKanters, Jørgen K.
dc.contributor.authorStrumke, Inga
dc.contributor.authorHalvorsen, Pål
dc.contributor.authorRiegler, Michael
dc.date.accessioned2023-09-06T14:06:57Z
dc.date.available2023-09-06T14:06:57Z
dc.date.issued2023-07-11
dc.description.abstractDeep neural networks are complex machine learning models that have shown promising results in analyzing high-dimensional data such as those collected from medical examinations. Such models have the potential to provide fast and accurate medical diagnoses. However, the high complexity makes deep neural networks and their predictions difficult to understand. Providing model explanations can be a way of increasing the understanding of “black box” models and building trust. In this work, we applied transfer learning to develop a deep neural network to predict sex from electrocardiograms. Using the visual explanation method Grad-CAM, heat maps were generated from the model in order to understand how it makes predictions. To evaluate the usefulness of the heat maps and determine if the heat maps identified electrocardiogram features that could be recognized to discriminate sex, medical doctors provided feedback. Based on the feedback, we concluded that, in our setting, this mode of explainable artificial intelligence does not provide meaningful information to medical doctors and is not useful in the clinic. Our results indicate that improved explanation techniques that are tailored to medical data should be developed before deep neural networks can be applied in the clinic for diagnostic purposes.en_US
dc.identifier.citationStorås, Andersen, Lockhart, Thielemann, Gnesin, Thambawita, Hicks, Kanters, Strumke, Halvorsen, Riegler. Usefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysis. Diagnostics (Basel). 2023;13(14)en_US
dc.identifier.cristinIDFRIDAID 2170562
dc.identifier.doi10.3390/diagnostics13142345
dc.identifier.issn2075-4418
dc.identifier.urihttps://hdl.handle.net/10037/30764
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.relation.journalDiagnostics (Basel)
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleUsefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysisen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's license is described as Attribution 4.0 International (CC BY 4.0)