Automated interpretation of time-lapse quantitative phase image by machine learning to study cellular dynamics during epithelial–mesenchymal transition

Significance: Machine learning is increasingly being applied to the classification of microscopic data. In order to detect some complex and dynamic cellular processes, time-resolved live-cell imaging might be necessary. Incorporating the temporal information into the classification process may allow...

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Detalles Bibliográficos
Autores principales: Strbkova, Lenka, Carson, Brittany B., Vincent, Theresa, Vesely, Pavel, Chmelik, Radim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Society of Photo-Optical Instrumentation Engineers 2020
Materias:
Microscopy
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7431880/
https://www.ncbi.nlm.nih.gov/pubmed/32812412
http://dx.doi.org/10.1117/1.JBO.25.8.086502

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7431880/
https://www.ncbi.nlm.nih.gov/pubmed/32812412
http://dx.doi.org/10.1117/1.JBO.25.8.086502

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