DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jo, YoungJu | ko |
dc.contributor.author | Cho, Hyungjoo | ko |
dc.contributor.author | Lee, Sang Yun | ko |
dc.contributor.author | Choi, Gunho | ko |
dc.contributor.author | Kim, Geon | ko |
dc.contributor.author | Min, Hyun-seok | ko |
dc.contributor.author | Park, YongKeun | ko |
dc.date.accessioned | 2018-10-19T00:28:23Z | - |
dc.date.available | 2018-10-19T00:28:23Z | - |
dc.date.created | 2018-09-19 | - |
dc.date.created | 2018-09-19 | - |
dc.date.created | 2018-09-19 | - |
dc.date.created | 2018-09-19 | - |
dc.date.created | 2018-09-19 | - |
dc.date.issued | 2019-01 | - |
dc.identifier.citation | IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS, v.25, no.1, pp.6800914 | - |
dc.identifier.issn | 1077-260X | - |
dc.identifier.uri | http://hdl.handle.net/10203/245859 | - |
dc.description.abstract | Recent 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.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Quantitative Phase Imaging and Artificial Intelligence: A Review | - |
dc.type | Article | - |
dc.identifier.wosid | 000443471200001 | - |
dc.identifier.scopusid | 2-s2.0-85050977684 | - |
dc.type.rims | ART | - |
dc.citation.volume | 25 | - |
dc.citation.issue | 1 | - |
dc.citation.beginningpage | 6800914 | - |
dc.citation.publicationname | IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS | - |
dc.identifier.doi | 10.1109/JSTQE.2018.2859234 | - |
dc.contributor.localauthor | Park, YongKeun | - |
dc.contributor.nonIdAuthor | Jo, YoungJu | - |
dc.contributor.nonIdAuthor | Cho, Hyungjoo | - |
dc.contributor.nonIdAuthor | Choi, Gunho | - |
dc.contributor.nonIdAuthor | Min, Hyun-seok | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Review | - |
dc.subject.keywordAuthor | Artificial intelligence | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | biomedical imaging | - |
dc.subject.keywordAuthor | microscopy | - |
dc.subject.keywordAuthor | optics | - |
dc.subject.keywordAuthor | quantitative phase imaging | - |
dc.subject.keywordPlus | DIGITAL HOLOGRAPHIC MICROSCOPY | - |
dc.subject.keywordPlus | OPTICAL DIFFRACTION TOMOGRAPHY | - |
dc.subject.keywordPlus | REFRACTIVE-INDEX TOMOGRAPHY | - |
dc.subject.keywordPlus | CONVOLUTIONAL NEURAL-NETWORKS | - |
dc.subject.keywordPlus | CELL IDENTIFICATION | - |
dc.subject.keywordPlus | AUTOMATIC IDENTIFICATION | - |
dc.subject.keywordPlus | LESION SEGMENTATION | - |
dc.subject.keywordPlus | INVERSE PROBLEMS | - |
dc.subject.keywordPlus | TIME | - |
dc.subject.keywordPlus | ALGORITHMS | - |
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