Deep Learning in Biological Image and Signal Processing

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dc.contributor.authorMeijering, Erikko
dc.contributor.authorCalhoun, Vince D.ko
dc.contributor.authorMenegaz, Gloriako
dc.contributor.authorMiller, David J.ko
dc.contributor.authorYe, Jong Chulko
dc.date.accessioned2022-04-14T06:44:06Z-
dc.date.available2022-04-14T06:44:06Z-
dc.date.created2022-03-21-
dc.date.created2022-03-21-
dc.date.created2022-03-21-
dc.date.created2022-03-21-
dc.date.created2022-03-21-
dc.date.issued2022-03-
dc.identifier.citationIEEE SIGNAL PROCESSING MAGAZINE, v.39, no.2, pp.24 - 26-
dc.identifier.issn1053-5888-
dc.identifier.urihttp://hdl.handle.net/10203/292761-
dc.description.abstractBiological research on the fundamental structural and functional properties of life - from molecules to cells, tissues, organs, and complete organisms, including human life - relies heavily on advanced imaging systems and measurement devices generating data of ever-increasing quantity and complexity. Automated processing and analysis of these data through increasingly sophisticated computational methods have become indispensable in exploiting relevant information and enabling researchers to detect patterns that may be unnoticeable to human senses. © 1991-2012 IEEE.-
dc.languageEnglish-
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC-
dc.titleDeep Learning in Biological Image and Signal Processing-
dc.typeArticle-
dc.identifier.wosid000761217500011-
dc.identifier.scopusid2-s2.0-85125599274-
dc.type.rimsART-
dc.citation.volume39-
dc.citation.issue2-
dc.citation.beginningpage24-
dc.citation.endingpage26-
dc.citation.publicationnameIEEE SIGNAL PROCESSING MAGAZINE-
dc.identifier.doi10.1109/MSP.2021.3134525-
dc.contributor.localauthorYe, Jong Chul-
dc.contributor.nonIdAuthorMeijering, Erik-
dc.contributor.nonIdAuthorCalhoun, Vince D.-
dc.contributor.nonIdAuthorMenegaz, Gloria-
dc.contributor.nonIdAuthorMiller, David J.-
dc.description.isOpenAccessN-
dc.type.journalArticleEditorial Material-
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