This repository introduces an Extreme Learning Machine (ELM)-based fluorescence lifetime imaging (FLIM) method. Fast training and inference were achieved. This ELM can extract individual fluorescence components from bi-exponential decays. It has a good performance on speed and accuracy.
Files explanation:
Synthetic_TrainData_bi_decay1.mat: Training (Synthetic) data of mono-exponential decays.
Synthetic_TestData_single_decay.mat: Test (Synehetic) data for mono-exponential decays.
Synthetic_TrainData_bi_decay1.mat: Training (Synthetic) data of bi-exponential decays.
Synthetic_TestData_bi_decay.mat: Test (Synehetic) data for bi-exponential decays.
cluster_100_cycle.mat: Real prostatic cell data coated with gold nanoprobe.
TrainNTestELM.m: Training and testing file, including implementation of NLSF and generation of synthetic IRF.
DataGen.m: Basic mono- and bi-exponential decays generation.
irf.m: Instrument response function (IRF).
For more details about the ELM, comparisons, and optics setups, please refer to the paper. Please cite this paper if you find it useful. Thanks.
Citation:
Zang, Z., Xiao, D., Wang, Q., LI, Z., Xie, W., Chen, Y., & Li, D. D. U. (2022). Fast analysis of time‐domain fluorescence lifetime imaging via extreme learning machine. Sensors, 22(10), [3758]. https://doi.org/10.3390/s22103758