通过极限学习机进行快速荧光寿命成像分析
Fast fluorescence lifetime imaging analysis via extreme learning machine
摘要：
我们提出了一种快速，准确的分析方法，荧光寿命成像显微镜（FLIM）使用极端学习机（ELM）。我们使用了广泛的指标来评估ELM和现有的算法。首先，我们使用合成数据集比较了这些算法。结果表明，ELM可以获得更高的保真度，即使在低光子条件下。之后，我们使用ELM从装载有金纳米传感器的人前列腺癌细胞中检索寿命成分，表明ELM也优于迭代拟合和非拟合算法。通过将ELM与计算效率高的神经网络进行比较，ELM以更少的训练和推理时间实现了相当的准确性。由于ELM在训练阶段没有反向传播过程，因此训练速度远远高于现有的神经网络方法。所提出的策略是有前途的边缘计算与在线训练。
英文摘要：
We present a fast and accurate analytical method for fluorescence lifetime imaging microscopy (FLIM) using the extreme learning machine (ELM). We used extensive metrics to evaluate ELM and existing algorithms. First, we compared these algorithms using synthetic datasets. Results indicate that ELM can obtain higher fidelity, even in low-photon conditions. Afterwards, we used ELM to retrieve lifetime components from human prostate cancer cells loaded with gold nanosensors, showing that ELM also outperforms the iterative fitting and non-fitting algorithms. By comparing ELM with a computational efficient neural network, ELM achieves comparable accuracy with less training and inference time. As there is no back-propagation process for ELM during the training phase, the training speed is much higher than existing neural network approaches. The proposed strategy is promising for edge computing with online training.
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