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- W3135775057 abstract "Label-free phenotypic classification at a single-cell level is a challenging yet important task in cell biology. Stimulated Raman scattering (SRS) microscopy provides high chemical selectivity and sensitivity for label-free imaging of biological samples. With the capability to record hyperspectral SRS images with high-speed, mapping of biomolecules inside living cells enables label-free phenotyping. However, like all high-dimensional data, it remains challenging to fully exploit the excessive amount of information contained in hyperspectral data for single-cell analysis. Here, we developed and compared two machine-learning-based methods - the convolutional neural network and support vector machine - to automatically extract important features from high dimensional data and achieve a high-accuracy label-free single-cell classification. These methods serve as a robust approach to classify cells based on their molecular features, allowing unbiased, high-throughput data analysis." @default.
- W3135775057 created "2021-03-15" @default.
- W3135775057 creator A5027132098 @default.
- W3135775057 date "2021-03-05" @default.
- W3135775057 modified "2023-09-23" @default.
- W3135775057 title "Robust single-cell classification in hyperspectral stimulated raman scattering imaging by machine learning" @default.
- W3135775057 doi "https://doi.org/10.1117/12.2585438" @default.
- W3135775057 hasPublicationYear "2021" @default.
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