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- Publisher Website: 10.1364/MICROSCOPY.2020.MTu2A.3
- Scopus: eid_2-s2.0-85091394362
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Conference Paper: Robust Quantitative Phase Imaging Cytometry with Transfer Learning
| Title | Robust Quantitative Phase Imaging Cytometry with Transfer Learning |
|---|---|
| Authors | |
| Issue Date | 2020 |
| Publisher | Optical Society of America. |
| Citation | OSA Biophotonics Congress: Biomedical Optics: Microscopy Histopathology and Analytics 2020, Washington, DC, USA, 20–23 April 2020. In Proceedings Biophotonics Congress: Biomedical Optics 2020 (Translational, Microscopy, OCT, OTS, BRAIN), paper MTu2A.3 How to Cite? |
| Abstract | We present high-throughput quantitative phase imaging cytometry (>10,000 cells/sec) assisted by neural-networked-based transfer learning that critically overcomes the batch effects and enables accurate label-free multi-class lung cancer types classification at single-cell precision (>91%).
© 2020 The Author(s) |
| Description | Session: Machine Learning II (MTu2A) - paper MTu2A.3 OSA Technical Digest (Optical Society of America, 2020) |
| Persistent Identifier | http://hdl.handle.net/10722/294231 |
| ISBN |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Lo, MCK | - |
| dc.contributor.author | Stassen, SV | - |
| dc.contributor.author | Siu, DMD | - |
| dc.contributor.author | Tsia, KKM | - |
| dc.date.accessioned | 2020-11-23T08:28:18Z | - |
| dc.date.available | 2020-11-23T08:28:18Z | - |
| dc.date.issued | 2020 | - |
| dc.identifier.citation | OSA Biophotonics Congress: Biomedical Optics: Microscopy Histopathology and Analytics 2020, Washington, DC, USA, 20–23 April 2020. In Proceedings Biophotonics Congress: Biomedical Optics 2020 (Translational, Microscopy, OCT, OTS, BRAIN), paper MTu2A.3 | - |
| dc.identifier.isbn | 9781943580743 | - |
| dc.identifier.uri | http://hdl.handle.net/10722/294231 | - |
| dc.description | Session: Machine Learning II (MTu2A) - paper MTu2A.3 | - |
| dc.description | OSA Technical Digest (Optical Society of America, 2020) | - |
| dc.description.abstract | We present high-throughput quantitative phase imaging cytometry (>10,000 cells/sec) assisted by neural-networked-based transfer learning that critically overcomes the batch effects and enables accurate label-free multi-class lung cancer types classification at single-cell precision (>91%). © 2020 The Author(s) | - |
| dc.language | eng | - |
| dc.publisher | Optical Society of America. | - |
| dc.relation.ispartof | Biophotonics Congress: Biomedical Optics 2020 (Translational, Microscopy, OCT, OTS, BRAIN) | - |
| dc.rights | Biophotonics Congress: Biomedical Optics 2020 (Translational, Microscopy, OCT, OTS, BRAIN). Copyright © Optical Society of America. | - |
| dc.title | Robust Quantitative Phase Imaging Cytometry with Transfer Learning | - |
| dc.type | Conference_Paper | - |
| dc.identifier.email | Stassen, SV: shobana@hku.hk | - |
| dc.identifier.email | Tsia, KKM: tsia@hku.hk | - |
| dc.identifier.authority | Tsia, KKM=rp01389 | - |
| dc.description.nature | link_to_subscribed_fulltext | - |
| dc.identifier.doi | 10.1364/MICROSCOPY.2020.MTu2A.3 | - |
| dc.identifier.scopus | eid_2-s2.0-85091394362 | - |
| dc.identifier.hkuros | 319047 | - |
| dc.identifier.spage | paper MTu2A.3 | - |
| dc.identifier.epage | paper MTu2A.3 | - |
| dc.publisher.place | United States | - |