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Conference Paper: Deep convolutional neural network for single-cell image analysis
Title | Deep convolutional neural network for single-cell image analysis |
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Authors | |
Issue Date | 2018 |
Publisher | SPIE - International Society for Optical Engineering. The Journal's web site is located at https://www.spiedigitallibrary.org/conference-proceedings-of-spie |
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, article no. 105050K How to Cite? |
Abstract | Single-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. |
Description | Conference Presentation Recording |
Persistent Identifier | http://hdl.handle.net/10722/259704 |
ISBN | |
ISSN | 2023 SCImago Journal Rankings: 0.152 |
DC Field | Value | Language |
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dc.contributor.author | Lam, EYM | - |
dc.contributor.author | Meng, N | - |
dc.contributor.author | So, HKH | - |
dc.date.accessioned | 2018-09-03T04:12:29Z | - |
dc.date.available | 2018-09-03T04:12:29Z | - |
dc.date.issued | 2018 | - |
dc.identifier.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, article no. 105050K | - |
dc.identifier.isbn | 9781510614956 | - |
dc.identifier.issn | 0277-786X | - |
dc.identifier.uri | http://hdl.handle.net/10722/259704 | - |
dc.description | Conference Presentation Recording | - |
dc.description.abstract | Single-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.language | eng | - |
dc.publisher | SPIE - International Society for Optical Engineering. The Journal's web site is located at https://www.spiedigitallibrary.org/conference-proceedings-of-spie | - |
dc.relation.ispartof | Proceedings of SPIE | - |
dc.title | Deep convolutional neural network for single-cell image analysis | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Lam, EYM: elam@eee.hku.hk | - |
dc.identifier.email | So, HKH: hso@eee.hku.hk | - |
dc.identifier.authority | Lam, EYM=rp00131 | - |
dc.identifier.authority | So, HKH=rp00169 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1117/12.2295469 | - |
dc.identifier.hkuros | 288782 | - |
dc.identifier.volume | 10505 | - |
dc.identifier.spage | article no. 105050K | - |
dc.identifier.epage | article no. 105050K | - |
dc.identifier.issnl | 0277-786X | - |