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dc.contributor.authorOrdonez, Alba
dc.contributor.authorEikvil, Line
dc.contributor.authorSalberg, Arnt-Børre
dc.contributor.authorHarbitz, Alf
dc.contributor.authorMurray, Sean Meling
dc.contributor.authorKampffmeyer, Michael
dc.date.accessioned2020-08-06T10:49:12Z
dc.date.available2020-08-06T10:49:12Z
dc.date.issued2020-06-19
dc.description.abstractAge-reading of fish otoliths (ear stones) is important for the sustainable management of fish resources. However, the procedure is challenging and requires experienced readers to carefully examine annual growth zones. In a recent study, convolutional neural networks (CNNs) have been demonstrated to perform reasonably well on automatically predicting fish age from otolith images. In the present study, we carefully investigate the prediction rule learned by such neural networks to provide insight into the features that identify certain fish age ranges. For this purpose, a recent technique for visualizing and analyzing the predictions of the neural networks was applied to different versions of the otolith images. The results indicate that supplementary knowledge about the internal structure improves the results for the youngest age groups, compared to using only the contour shape attribute of the otolith. However, the contour shape and size attributes are, in general, sufficient for older age groups. In addition, within specific age ranges we find that the network tends to focus on particular areas of the otoliths and that the most discriminating factors seem to be related to the central part and the outer edge of the otolith. Explaining age predictions from otolith images as done in this study will hopefully help build confidence in the potential of deep learning algorithms for automatic age prediction, as well as improve the quality of the age estimation.en_US
dc.identifier.citationOrdonez, Eikvil, Salberg, Harbitz, Murray, Kampffmeyer. Explaining decisions of deep neural networks used for fish age prediction. PLOS ONE. 2020;15(6)en_US
dc.identifier.cristinIDFRIDAID 1819962
dc.identifier.doi10.1371/journal.pone.0235013
dc.identifier.issn1932-6203
dc.identifier.urihttps://hdl.handle.net/10037/18922
dc.language.isoengen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.journalPLOS ONE
dc.relation.projectIDinfo:eu-repo/grantAgreement/RCN/IKTPLUSS/ 270966/Norway/Ubiquitous cognitive computer vision for marine services//en_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2020 The Author(s)en_US
dc.subjectVDP::Mathematics and natural science: 400::Physics: 430en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Fysikk: 430en_US
dc.titleExplaining decisions of deep neural networks used for fish age predictionen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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