Machine learning issues and opportunities in ultrafast particle classification for label-free microflow cytometry.
Sci Rep 2020;
10:20724. [PMID:
33244129 PMCID:
PMC7691359 DOI:
10.1038/s41598-020-77765-w]
[Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Accepted: 11/12/2020] [Indexed: 11/11/2022] Open
Abstract
Machine learning offers promising solutions for high-throughput single-particle analysis in label-free imaging microflow cytomtery. However, the throughput of online operations such as cell sorting is often limited by the large computational cost of the image analysis while offline operations may require the storage of an exceedingly large amount of data. Moreover, the training of machine learning systems can be easily biased by slight drifts of the measurement conditions, giving rise to a significant but difficult to detect degradation of the learned operations. We propose a simple and versatile machine learning approach to perform microparticle classification at an extremely low computational cost, showing good generalization over large variations in particle position. We present proof-of-principle classification of interference patterns projected by flowing transparent PMMA microbeads with diameters of \documentclass[12pt]{minimal}
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\begin{document}$${15.2}\,\upmu \text {m}$$\end{document}15.2μm and \documentclass[12pt]{minimal}
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\begin{document}$${18.6}\,\upmu \text {m}$$\end{document}18.6μm. To this end, a simple, cheap and compact label-free microflow cytometer is employed. We also discuss in detail the detection and prevention of machine learning bias in training and testing due to slight drifts of the measurement conditions. Moreover, we investigate the implications of modifying the projected particle pattern by means of a diffraction grating, in the context of optical extreme learning machine implementations.
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