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dc.contributor.authorThunold, Håvard Horgen
dc.contributor.authorRiegler, Michael
dc.contributor.authorYazidi, Anis
dc.contributor.authorHammer, Hugo Lewi
dc.date.accessioned2024-01-16T10:20:31Z
dc.date.available2024-01-16T10:20:31Z
dc.date.issued2023-11-09
dc.description.abstractAn important part of diagnostics is to gain insight into properties that characterize a disease. Machine learning has been used for this purpose, for instance, to identify biomarkers in genomics. However, when patient data are presented as images, identifying properties that characterize a disease becomes far more challenging. A common strategy involves extracting features from the images and analyzing their occurrence in healthy versus pathological images. A limitation of this approach is that the ability to gain new insights into the disease from the data is constrained by the information in the extracted features. Typically, these features are manually extracted by humans, which further limits the potential for new insights. To overcome these limitations, in this paper, we propose a novel framework that provides insights into diseases without relying on handcrafted features or human intervention. Our framework is based on deep learning (DL), explainable artificial intelligence (XAI), and clustering. DL is employed to learn deep patterns, enabling efficient differentiation between healthy and pathological images. Explainable artificial intelligence (XAI) visualizes these patterns, and a novel “explanation-weighted” clustering technique is introduced to gain an overview of these patterns across multiple patients. We applied the method to images from the gastrointestinal tract. In addition to real healthy images and real images of polyps, some of the images had synthetic shapes added to represent other types of pathologies than polyps. The results show that our proposed method was capable of organizing the images based on the reasons they were diagnosed as pathological, achieving high cluster quality and a rand index close to or equal to one.en_US
dc.identifier.citationThunold, Riegler M, Yazidi A, Hammer HL. A Deep Diagnostic Framework Using Explainable Artificial Intelligence and Clustering. Diagnostics (Basel). 2023;13(22)en_US
dc.identifier.cristinIDFRIDAID 2192611
dc.identifier.doi10.3390/diagnostics13223413
dc.identifier.issn2075-4418
dc.identifier.urihttps://hdl.handle.net/10037/32508
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.titleA Deep Diagnostic Framework Using Explainable Artificial Intelligence and Clusteringen_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)