Abstract
As demonstrated in previous chapters, our TS-QPI system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. In this chapter, we use these biophysical measurements to form a hyperdimensional feature space in which supervised learning is performed for cell classification. We show that TS-QPI not only overcomes the throughput issue in cellular imaging, but also improves label-free diagnosis by integration of sensing multiple biophysical features. We also compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.
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Mahjoubfar, A., Chen, C.L., Jalali, B. (2017). Deep Learning and Classification. In: Artificial Intelligence in Label-free Microscopy. Springer, Cham. https://doi.org/10.1007/978-3-319-51448-2_8
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DOI: https://doi.org/10.1007/978-3-319-51448-2_8
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