On Learning from Ghost Imaging without Imaging
This work addresses the need for faster analysis in flow cytometry by proposing a method to skip image reconstruction, though it appears incremental as it builds on existing ghost cytometry techniques.
The paper tackles the bottleneck in high-speed cell classification by analyzing the theoretical feasibility of using ghost imaging signals directly for learning, bypassing image reconstruction.
Computational ghost imaging is an imaging technique in which an object is imaged from light collected using a single-pixel detector with no spatial resolution. Recently, ghost cytometry has been proposed for a high-speed cell-classification method that involves ghost imaging and machine learning in flow cytometry. Ghost cytometry skips the reconstruction of cell images from signals and directly used signals for cell-classification because this reconstruction is what creates the bottleneck in the high-speed analysis. In this paper, we provide theoretical analysis for learning from ghost imaging without imaging.