论文标题
通过极端学习机器快速荧光寿命成像分析
Fast fluorescence lifetime imaging analysis via extreme learning machine
论文作者
论文摘要
我们提出了一种使用极端学习机（ELM）的荧光寿命成像显微镜（FLIM）的快速准确的分析方法。我们使用广泛的指标来评估ELM和现有算法。首先，我们使用合成数据集比较了这些算法。结果表明，即使在低光子条件下，ELM也可以获得更高的保真度。之后，我们使用ELM从带有金纳米传感器的人类前列腺癌细胞中检索终生成分，表明ELM还表现出胜过迭代拟合和非拟合算法。通过将ELM与计算有效的神经网络进行比较，ELM可以通过更少的训练和推理时间来达到可比的精度。由于在训练阶段没有榆树的后传播过程，因此训练速度比现有的神经网络方法高得多。提出的策略有望通过在线培训进行边缘计算。
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.
