A weakly supervised deep learning approach for label-free imaging flow-cytometry-based blood diagnostics

The application of machine learning approaches to imaging flow cytometry (IFC) data has the potential to transform the diagnosis of hematological diseases. However, the need for manually labeled single-cell images for machine learning model training has severely limited its clinical application. To...

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Detalles Bibliográficos
Autores principales: Otesteanu, Corin F., Ugrinic, Martina, Holzner, Gregor, Chang, Yun-Tsan, Fassnacht, Christina, Guenova, Emmanuella, Stavrakis, Stavros, deMello, Andrew, Claassen, Manfred
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Report
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9017143/
https://www.ncbi.nlm.nih.gov/pubmed/35474892
http://dx.doi.org/10.1016/j.crmeth.2021.100094

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9017143/
https://www.ncbi.nlm.nih.gov/pubmed/35474892
http://dx.doi.org/10.1016/j.crmeth.2021.100094

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