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.

翻译：成像流式细胞术系统旨在根据大量细胞或微粒的物理特性对其进行分析。当前绝大多数系统通过采集大量图像来训练深度人工神经网络，然而这种方法会增加最终设备的延迟和功耗。在这项进展研究中，我们将事件相机与自由空间光学装置相结合，为流经微流控通道的每个粒子采集脉冲信号。基于收集的数据集训练脉冲神经网络，在完全基于事件的分类流程中，平均训练准确率达到97.7%，平均测试准确率达到93.5%。