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Conference Paper: Deep convolutional neural network for single-cell image analysis

TitleDeep convolutional neural network for single-cell image analysis
Authors
Issue Date2018
PublisherSPIE.
Citation
High-Speed Biomedical Imaging and Spectroscopy III: Toward Big Data Instrumentation and Management, San Francisco, CA, 29 - 30 January 2018. In Proceedings of SPIE, 2018, v. 10505, p. 12 How to Cite?
AbstractSingle-cell classification based on the cell’s visual images, i.e., their phenotypes, can greatly complement genomic-based techniques for anomaly detection, which in turn has the potential for assistance in early cancer diagnosis. A high-speed imaging system is often needed for capturing the individual cell images, and in addition, the process involves big data computation, as we often have a large amount of cells for analysis and classification. Here, we focus on the latter, where we devise a deep convolutional neural network and show its efficacy for the task.
Persistent Identifierhttp://hdl.handle.net/10722/259704
ISBN

 

DC FieldValueLanguage
dc.contributor.authorLam, EYM-
dc.contributor.authorMeng, N-
dc.contributor.authorSo, HKH-
dc.date.accessioned2018-09-03T04:12:29Z-
dc.date.available2018-09-03T04:12:29Z-
dc.date.issued2018-
dc.identifier.citationHigh-Speed Biomedical Imaging and Spectroscopy III: Toward Big Data Instrumentation and Management, San Francisco, CA, 29 - 30 January 2018. In Proceedings of SPIE, 2018, v. 10505, p. 12-
dc.identifier.isbn9781510614956-
dc.identifier.urihttp://hdl.handle.net/10722/259704-
dc.description.abstractSingle-cell classification based on the cell’s visual images, i.e., their phenotypes, can greatly complement genomic-based techniques for anomaly detection, which in turn has the potential for assistance in early cancer diagnosis. A high-speed imaging system is often needed for capturing the individual cell images, and in addition, the process involves big data computation, as we often have a large amount of cells for analysis and classification. Here, we focus on the latter, where we devise a deep convolutional neural network and show its efficacy for the task.-
dc.languageeng-
dc.publisherSPIE.-
dc.relation.ispartofProceedings of SPIE-
dc.rightsProceedings of SPIE. Copyright © SPIE.-
dc.rightsCopyright notice format: Copyright XXXX (year) Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.-
dc.titleDeep convolutional neural network for single-cell image analysis-
dc.typeConference_Paper-
dc.identifier.emailLam, EYM: elam@eee.hku.hk-
dc.identifier.emailSo, HKH: hso@eee.hku.hk-
dc.identifier.authorityLam, EYM=rp00131-
dc.identifier.authoritySo, HKH=rp00169-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1117/12.2295469-
dc.identifier.hkuros288782-
dc.identifier.volume10505-
dc.identifier.spage12-
dc.identifier.epage12-

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