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

• URL: http://arxiv.org/abs/2303.10632v1
• Date: Sun, 19 Mar 2023 11:32:57 GMT
• Title: Training a spiking neural network on an event-based label-free flow cytometry dataset
• Authors: Muhammed Gouda, Steven Abreu, Alessio Lugnan, Peter Bienstman
• Abstract summary: In this work, 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.
• Score: 0.7742297876120561
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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