Training a spiking neural network on an event-based label-free flow cytometry dataset
This work addresses efficiency issues in flow cytometry for biomedical analysis, but it is incremental as it builds on existing event-based and spiking neural network methods.
The researchers tackled the problem of high latency and power consumption in imaging flow cytometry by using an event-based camera and spiking neural network, achieving 97.7% training accuracy and 93.5% testing accuracy.
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.