Cancer-Cell Deep-Learning Classification by Integrating Quantitative-Phase Spatial and Temporal Fluctuations

We present a new classification approach for live cells, integrating together the spatial and temporal fluctuation maps and the quantitative optical thickness map of the cell, as acquired by common-path quantitative-phase dynamic imaging and processed with a deep-learning framework. We demonstrate t...

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Detalles Bibliográficos
Autores principales: Ben Baruch, Shani, Rotman-Nativ, Noa, Baram, Alon, Greenspan, Hayit, Shaked, Natan T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8699730/
https://www.ncbi.nlm.nih.gov/pubmed/34943859
http://dx.doi.org/10.3390/cells10123353

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8699730/
https://www.ncbi.nlm.nih.gov/pubmed/34943859
http://dx.doi.org/10.3390/cells10123353

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