Fast fluorescence lifetime imaging analysis via extreme learning machine

TL;DR

This paper introduces a rapid and precise fluorescence lifetime imaging analysis method using extreme learning machine (ELM), outperforming existing algorithms in accuracy and speed, suitable for real-time edge computing applications.

Contribution

The paper presents a novel application of ELM for FLIM analysis, demonstrating superior speed and accuracy over traditional methods and neural networks without back-propagation.

Findings

ELM achieves higher fidelity in low-photon conditions

ELM outperforms iterative fitting and non-fitting algorithms in biological samples

ELM has comparable accuracy with less training and inference time

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.