Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Rehman, Abdur | - |
dc.contributor.author | An, Hyunbin | - |
dc.contributor.author | Park, Seonghwan | - |
dc.contributor.author | Moon, Inkyu | - |
dc.date.accessioned | 2024-06-03T13:10:12Z | - |
dc.date.available | 2024-06-03T13:10:12Z | - |
dc.date.created | 2024-02-20 | - |
dc.date.issued | 2024-07 | - |
dc.identifier.issn | 0030-3992 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11750/56627 | - |
dc.description.abstract | Image-based stain-free elliptical cancer cell classification is challenging, due to the inter-class morphological similarity. In this paper, we address the classification of different types of cancer cell lines (lung, breast, bladder, and skin) by utilizing self-supervised learning, and compare it with supervised learning based on convolutional neural network. Digital holography in a microscopic configuration was used to obtain stain-free quantitative phase images representing the intracellular content and morphology of cells. The performance of self-supervised learning in natural images shows promising results, and consistently closes the gap between self-supervised and supervised learning. The ability of self-supervised learning to effectively utilize unlabeled data for training is instrumental in the biomedical domain, where labeled data is scarce. Our goal is to study different self-supervised frameworks of biomedical holographic data, and determine how they can be utilized to advance liquid biopsy for the detection of cancer cells. After extensive experimentation, we conclude that self-supervised learning improves the classification performance on cancer cell datasets, and outperforms supervised learning when training data is limited, which is mostly the case in biomedical imaging. © 2024 Elsevier Ltd | - |
dc.language | English | - |
dc.publisher | Elsevier | - |
dc.title | Automated classification of elliptical cancer cells with stain-free holographic imaging and self-supervised learning | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.optlastec.2024.110646 | - |
dc.identifier.wosid | 001178601200001 | - |
dc.identifier.scopusid | 2-s2.0-85184137454 | - |
dc.identifier.bibliographicCitation | Optics and Laser Technology, v.174 | - |
dc.description.isOpenAccess | FALSE | - |
dc.subject.keywordAuthor | Holographic cell imaging | - |
dc.subject.keywordAuthor | Classification of cancer cells | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Self -supervised learning | - |
dc.subject.keywordAuthor | Stain -free analysis of cancer cells | - |
dc.subject.keywordPlus | MICROSCOPY | - |
dc.subject.keywordPlus | IDENTIFICATION | - |
dc.citation.title | Optics and Laser Technology | - |
dc.citation.volume | 174 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Optics; Physics | - |
dc.relation.journalWebOfScienceCategory | Optics; Physics, Applied | - |
dc.type.docType | Article | - |
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