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- Publisher Website: 10.23919/MVA.2017.7986833
- Scopus: eid_2-s2.0-85027845354
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Conference Paper: Computational single-cell classification using deep learning on bright-field and phase images
Title | Computational single-cell classification using deep learning on bright-field and phase images |
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Authors | |
Issue Date | 2017 |
Publisher | IEEE. The Proceedings' web site is located at https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7981294 |
Citation | Proceedings of the Fifteenth IAPR International Conference on Machine Vision Applications (IAPR MVA2017), Nagoya University, Nagoya, Japan, 8-12 May 2017, p. 190-193 How to Cite? |
Abstract | Automated cell classification is an important machine vision problem with significant benefits to biomedicine. We propose an efficient high-accuracy framework to classify cells based on bright-field and phase images using deep learning. With carefully designed network architecture and parameters, our network extracts features from single-cell images hierarchically and performs classification jointly. It can identify different types of cells without any human intervention and biological or hand-crafted features. Our experiments show that the system achieves a mean class accuracy of 96.5% on the single-cell images captured by an ultrafast time-stretch imager. |
Description | Session 06: Recognition/Classification - no. 06-01 |
Persistent Identifier | http://hdl.handle.net/10722/245530 |
DC Field | Value | Language |
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dc.contributor.author | Meng, N | - |
dc.contributor.author | So, HKH | - |
dc.contributor.author | Lam, EYM | - |
dc.date.accessioned | 2017-09-18T02:12:20Z | - |
dc.date.available | 2017-09-18T02:12:20Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | Proceedings of the Fifteenth IAPR International Conference on Machine Vision Applications (IAPR MVA2017), Nagoya University, Nagoya, Japan, 8-12 May 2017, p. 190-193 | - |
dc.identifier.uri | http://hdl.handle.net/10722/245530 | - |
dc.description | Session 06: Recognition/Classification - no. 06-01 | - |
dc.description.abstract | Automated cell classification is an important machine vision problem with significant benefits to biomedicine. We propose an efficient high-accuracy framework to classify cells based on bright-field and phase images using deep learning. With carefully designed network architecture and parameters, our network extracts features from single-cell images hierarchically and performs classification jointly. It can identify different types of cells without any human intervention and biological or hand-crafted features. Our experiments show that the system achieves a mean class accuracy of 96.5% on the single-cell images captured by an ultrafast time-stretch imager. | - |
dc.language | eng | - |
dc.publisher | IEEE. The Proceedings' web site is located at https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7981294 | - |
dc.relation.ispartof | IAPR Conference on Machine Vision Applications | - |
dc.rights | IAPR Conference on Machine Vision Applications. Copyright © IEEE. | - |
dc.title | Computational single-cell classification using deep learning on bright-field and phase images | - |
dc.type | Conference_Paper | - |
dc.identifier.email | So, HKH: hso@eee.hku.hk | - |
dc.identifier.email | Lam, EYM: elam@eee.hku.hk | - |
dc.identifier.authority | So, HKH=rp00169 | - |
dc.identifier.authority | Lam, EYM=rp00131 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.23919/MVA.2017.7986833 | - |
dc.identifier.scopus | eid_2-s2.0-85027845354 | - |
dc.identifier.hkuros | 277490 | - |
dc.identifier.hkuros | 295139 | - |
dc.identifier.spage | 190 | - |
dc.identifier.epage | 193 | - |
dc.publisher.place | United States | - |