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dc.contributor.authorAnnala, Leevi
dc.contributor.authorNeittaanmäki, Noora
dc.contributor.authorPaoli, John
dc.contributor.authorZaar, Oscar
dc.contributor.authorPölönen, Ilkka
dc.date.accessioned2020-10-13T08:47:56Z
dc.date.available2020-10-13T08:47:56Z
dc.date.issued2020
dc.identifier.citationAnnala, L., Neittaanmäki, N., Paoli, J., Zaar, O., & Pölönen, I. (2020). Generating Hyperspectral Skin Cancer Imagery using Generative Adversarial Neural Network. In <i>EMBC 2020 : Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society</i> (pp. 1600-1603). IEEE. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. <a href="https://doi.org/10.1109/EMBC44109.2020.9176292" target="_blank">https://doi.org/10.1109/EMBC44109.2020.9176292</a>
dc.identifier.otherCONVID_41828599
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/72130
dc.description.abstractIn this study we develop a proof of concept of using generative adversarial neural networks in hyperspectral skin cancer imagery production. Generative adversarial neural network is a neural network, where two neural networks compete. The generator tries to produce data that is similar to the measured data, and the discriminator tries to correctly classify the data as fake or real. This is a reinforcement learning model, where both models get reinforcement based on their performance. In the training of the discriminator we use data measured from skin cancer patients. The aim for the study is to develop a generator for augmenting hyperspectral skin cancer imagery.en
dc.format.mimetypeapplication/pdf
dc.languageeng
dc.language.isoeng
dc.publisherIEEE
dc.relation.ispartofEMBC 2020 : Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
dc.relation.ispartofseriesAnnual International Conference of the IEEE Engineering in Medicine and Biology Society
dc.rightsIn Copyright
dc.subject.othergenerative adversarial neural networks
dc.subject.otherskin cancer
dc.titleGenerating Hyperspectral Skin Cancer Imagery using Generative Adversarial Neural Network
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-202010136188
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.relation.isbn978-1-7281-1991-5
dc.description.reviewstatuspeerReviewed
dc.format.pagerange1600-1603
dc.relation.issn2375-7477
dc.type.versionacceptedVersion
dc.rights.copyright© IEEE, 2020
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceAnnual International Conference of the IEEE Engineering in Medicine and Biology Society
dc.relation.grantnumber314519
dc.subject.ysoihosyöpä
dc.subject.ysoneuroverkot
dc.subject.ysospektrikuvaus
dc.subject.ysokuvantaminen
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p13613
jyx.subject.urihttp://www.yso.fi/onto/yso/p7292
jyx.subject.urihttp://www.yso.fi/onto/yso/p26364
jyx.subject.urihttp://www.yso.fi/onto/yso/p3532
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1109/EMBC44109.2020.9176292
dc.relation.funderSuomen Akatemiafi
dc.relation.funderAcademy of Finlanden
jyx.fundingprogramAkatemiaohjelma, SAfi
jyx.fundingprogramAcademy Programme, AoFen
jyx.fundinginformationThis research was partly funded by Academy of Finland (grant: 314519).


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