# On Learning from Ghost Imaging without Imaging

## TL;DR

This paper provides a theoretical analysis of learning directly from ghost imaging signals, bypassing the image reconstruction step to enable faster, high-speed cell classification in flow cytometry.

## Contribution

It introduces a novel theoretical framework for learning from ghost imaging data without reconstructing images, addressing the bottleneck in high-speed analysis.

## Key findings

- Theoretical insights into direct learning from ghost imaging signals
- Potential for faster cell classification in flow cytometry
- Elimination of image reconstruction bottleneck

## Abstract

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.