Quantitative Phase Imaging and Artificial Intelligence: A Review

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dc.contributor.authorJo, YoungJuko
dc.contributor.authorCho, Hyungjooko
dc.contributor.authorLee, Sang Yunko
dc.contributor.authorChoi, Gunhoko
dc.contributor.authorKim, Geonko
dc.contributor.authorMin, Hyun-seokko
dc.contributor.authorPark, YongKeunko
dc.date.accessioned2018-10-19T00:28:23Z-
dc.date.available2018-10-19T00:28:23Z-
dc.date.created2018-09-19-
dc.date.created2018-09-19-
dc.date.created2018-09-19-
dc.date.created2018-09-19-
dc.date.created2018-09-19-
dc.date.issued2019-01-
dc.identifier.citationIEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS, v.25, no.1, pp.6800914-
dc.identifier.issn1077-260X-
dc.identifier.urihttp://hdl.handle.net/10203/245859-
dc.description.abstractRecent advances in quantitative phase imaging (QPI) and artificial intelligence (AI) have opened up the possibility of an exciting frontier. The fast and label-free nature of QPI enables the rapid generation of large-scale and uniform-quality imaging data in two, three, and four dimensions. Subsequently, the AI-assisted interrogation ofQPI data using data-driven machine learning techniques results in a variety of biomedical applications. Also, machine learning enhances QPI itself. Herein, we review the synergy between QPI and machine learning with a particular focus on deep learning. Furthermore, we provide practical guidelines and perspectives for further development.-
dc.languageEnglish-
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC-
dc.titleQuantitative Phase Imaging and Artificial Intelligence: A Review-
dc.typeArticle-
dc.identifier.wosid000443471200001-
dc.identifier.scopusid2-s2.0-85050977684-
dc.type.rimsART-
dc.citation.volume25-
dc.citation.issue1-
dc.citation.beginningpage6800914-
dc.citation.publicationnameIEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS-
dc.identifier.doi10.1109/JSTQE.2018.2859234-
dc.contributor.localauthorPark, YongKeun-
dc.contributor.nonIdAuthorJo, YoungJu-
dc.contributor.nonIdAuthorCho, Hyungjoo-
dc.contributor.nonIdAuthorChoi, Gunho-
dc.contributor.nonIdAuthorMin, Hyun-seok-
dc.description.isOpenAccessN-
dc.type.journalArticleReview-
dc.subject.keywordAuthorArtificial intelligence-
dc.subject.keywordAuthormachine learning-
dc.subject.keywordAuthorbiomedical imaging-
dc.subject.keywordAuthormicroscopy-
dc.subject.keywordAuthoroptics-
dc.subject.keywordAuthorquantitative phase imaging-
dc.subject.keywordPlusDIGITAL HOLOGRAPHIC MICROSCOPY-
dc.subject.keywordPlusOPTICAL DIFFRACTION TOMOGRAPHY-
dc.subject.keywordPlusREFRACTIVE-INDEX TOMOGRAPHY-
dc.subject.keywordPlusCONVOLUTIONAL NEURAL-NETWORKS-
dc.subject.keywordPlusCELL IDENTIFICATION-
dc.subject.keywordPlusAUTOMATIC IDENTIFICATION-
dc.subject.keywordPlusLESION SEGMENTATION-
dc.subject.keywordPlusINVERSE PROBLEMS-
dc.subject.keywordPlusTIME-
dc.subject.keywordPlusALGORITHMS-
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