Cargando…
Rapid, label-free classification of tumor-reactive T cell killing with quantitative phase microscopy and machine learning
Quantitative phase microscopy (QPM) enables studies of living biological systems without exogenous labels. To increase the utility of QPM, machine-learning methods have been adapted to extract additional information from the quantitative phase data. Previous QPM approaches focused on fluid flow syst...
Autores principales: | Kim, Diane N. H., Lim, Alexander A., Teitell, Michael A. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8484462/ https://www.ncbi.nlm.nih.gov/pubmed/34593878 http://dx.doi.org/10.1038/s41598-021-98567-8 |
Ejemplares similares
-
PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning
por: Rivenson, Yair, et al.
Publicado: (2019) -
Label-free deep learning-based species classification of bacteria imaged by phase-contrast microscopy
por: Hallström, Erik, et al.
Publicado: (2023) -
Label-free macrophage phenotype classification using machine learning methods
por: Hourani, Tetiana, et al.
Publicado: (2023) -
Digital Holographic Microscopy: A Quantitative Label-Free Microscopy Technique for Phenotypic Screening
por: Rappaz, Benjamin, et al.
Publicado: (2014) -
Label-free classification of neurons and glia in neural stem cell cultures using a hyperspectral imaging microscopy combined with machine learning
por: Ogi, Hiroshi, et al.
Publicado: (2019)