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dc.contributor.authorSyed, Shaheen
dc.contributor.authorAnderssen, Kathryn Elizabeth
dc.contributor.authorStormo, Svein Kristian
dc.contributor.authorKranz, Mathias
dc.date.accessioned2023-02-24T11:52:32Z
dc.date.available2023-02-24T11:52:32Z
dc.date.issued2023-02-13
dc.description.abstractFully supervised semantic segmentation models require pixel-level annotations that are costly to obtain. As a remedy, weakly supervised semantic segmentation has been proposed, where image-level labels and class activation maps (CAM) can detect discriminative regions for specific class objects. In this paper, we evaluated several CAM methods applied to different convolutional neural networks (CNN) to highlight tissue damage of cod fillets with soft boundaries in MRI. Our results show that different CAM methods produce very different CAM regions, even when applying them to the same CNN model. CAM methods that claim to highlight more of the class object do not necessarily highlight more damaged regions or originate from the same high discriminatory regions, nor do these damaged regions show high agreement across the different CAM methods. Additionally, CAM methods produce damaged regions that do not align with external reference metrics, and even show correlations contrary to what can be expected.en_US
dc.identifier.citationSyed, Anderssen, Stormo, Kranz. Weakly supervised semantic segmentation for MRI: exploring the advantages and disadvantages of class activation maps for biological image segmentation with soft boundaries. Scientific Reports. 2023;13en_US
dc.identifier.cristinIDFRIDAID 2127050
dc.identifier.doi10.1038/s41598-023-29665-y
dc.identifier.issn2045-2322
dc.identifier.urihttps://hdl.handle.net/10037/28609
dc.language.isoengen_US
dc.publisherSpringer Natureen_US
dc.relation.journalScientific Reports
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.titleWeakly supervised semantic segmentation for MRI: exploring the advantages and disadvantages of class activation maps for biological image segmentation with soft boundariesen_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)