  Imaging and analysis of many single cells hold great potential in our
understanding of heterogeneous and complex life systems and in enabling
biomedical applications. We here introduce a recently realized image-free
"imaging" cytometry technology, which we call ghost cytometry. While a
compressive ghost imaging technique utilizing object's motion relative to a
projected static light pattern allows recovery of their images, a key of this
ghost cytometry is to achieve ultrafast cell classification by directly
applying machine learning methods to the compressive imaging signals in a
temporal domain. We show the applicability of our method in the analysis of
flowing objects based on the reconstructed images as well as in that based on
the imaging waveform without image production.
