Training a spiking neural network on an event-based label-free flow cytometry dataset

TL;DR

This paper presents a novel event-based, label-free approach using a spiking neural network for flow cytometry, achieving high accuracy while reducing latency and power consumption.

Contribution

It introduces a new method combining event-based cameras with spiking neural networks for flow cytometry analysis, eliminating the need for large image datasets.

Findings

97.7% training accuracy

93.5% testing accuracy

Reduced latency and power consumption

Abstract

Imaging flow cytometry systems aim to analyze a huge number of cells or micro-particles based on their physical characteristics. The vast majority of current systems acquire a large amount of images which are used to train deep artificial neural networks. However, this approach increases both the latency and power consumption of the final apparatus. In this work-in-progress, we combine an event-based camera with a free-space optical setup to obtain spikes for each particle passing in a microfluidic channel. A spiking neural network is trained on the collected dataset, resulting in 97.7% mean training accuracy and 93.5% mean testing accuracy for the fully event-based classification pipeline.