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Quality control for high-throughput imaging experiments using machine learning in CellProfiler
Robust high-content screening of visual cellular phenotypes has been enabled by automated microscopy and quantitative image analysis. The identification and removal of common image-based aberrations is critical to the screening workflow. Out-of-focus images, debris, and auto-fluorescing samples can...
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| Publicado en: | Methods Mol Biol |
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| Autores principales: | , |
| Formato: | Artigo |
| Lenguaje: | Inglês |
| Publicado: |
2018
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| Materias: | |
| Acceso en línea: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6112602/ https://ncbi.nlm.nih.gov/pubmed/29082489 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1007/978-1-4939-7357-6_7 |
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