Fast fluorescence lifetime imaging analysis via extreme learning machine

• URL: http://arxiv.org/abs/2203.13754v1
• Date: Fri, 25 Mar 2022 16:34:51 GMT
• Title: Fast fluorescence lifetime imaging analysis via extreme learning machine
• Authors: Zhenya Zang, Dong Xiao, Quan Wang, Zinuo Li, Wujun Xie, Yu Chen, David Day Uei Li
• Abstract summary: We present a fast and accurate analytical method for fluorescence lifetime imaging microscopy (FLIM) using the extreme learning machine (ELM) Results indicate that ELM can obtain higher fidelity, even in low-photon conditions.
• Score: 7.7721777809498676
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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