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 \({15.2}\,\upmu \text {m}\) and \({18.6}\,\upmu \text {m}\). 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.

机器学习为无标签成像微流细胞术中的高通量单颗粒分析提供了有希望的解决方案。然而，诸如细胞分类的在线操作的吞吐量通常受到图像分析的大量计算成本的限制，而离线操作可能需要存储非常大量的数据。此外，机器学习系统的训练很容易因测量条件的微小偏差而产生偏差，从而导致学习操作的退化显着但难以检测。我们提出了一种简单而通用的机器学习方法，以极低的计算成本执行微粒分类，对微粒位置的大变化表现出良好的概括性。\（{15.2} \，\ upmu \ text {m} \）和\（{18.6} \，\ upmu \ text {m} \）。为此，采用了一种简单，便宜且紧凑的无标记微流式细胞仪。我们还将详细讨论在培训和测试中由于测量条件的轻微漂移而导致的机器学习偏差的检测和预防。此外，我们研究了在光学极限学习机实施方案中通过衍射光栅修改投影粒子图案的含义。