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 15.2Ã¬m and 18.6Ã¬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.
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