Photonics Research Group, UGent - imec, Technologiepark 126, 9052, Ghent, Belgium.
Center for Nano- and Biophotonics (NB-Photonics), Ghent University, Technologiepark 126, 9052, Ghent, Belgium.
Sci Rep. 2020 Nov 26;10(1):20724. doi: 10.1038/s41598-020-77765-w.
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 [Formula: see text] and [Formula: see text]. 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.
机器学习为无标记成像微流控中的高通量单颗粒分析提供了有前景的解决方案。然而，在线操作（如细胞分选）的通量通常受到图像分析的巨大计算成本的限制，而离线操作可能需要存储大量数据。此外，机器学习系统的训练很容易受到测量条件微小漂移的影响，从而导致学习操作的显著但难以检测的降级。我们提出了一种简单而通用的机器学习方法，以极低的计算成本进行微粒子分类，在粒子位置的大变化下表现出良好的泛化能力。我们提出了通过流动透明 PMMA 微球的干涉图案进行分类的原理证明，这些微球的直径为[Formula: see text]和[Formula: see text]。为此，我们采用了一种简单、廉价且紧凑的无标记微流控仪。我们还详细讨论了由于测量条件的微小漂移而在训练和测试中检测和防止机器学习偏差的问题。此外，我们还研究了通过衍射光栅修改投影粒子图案的影响，这是光学极限学习机实现的背景。
