ArticleSensors (Basel, Switzerland)2022
Fast Analysis of Time-Domain Fluorescence Lifetime Imaging via Extreme Learning Machine.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Label-free pathological subtyping of non-small cell lung cancer using deep classification and virtual immunohistochemical staining.NPJ digital medicine · 2026Article
- Fast blood flow index reconstruction of diffuse correlation spectroscopy using a back-propagation-free data-driven algorithm.Biomedical optics express · 2025Article
- Fluorescence Lifetime Endoscopy with a Nanosecond Time-Gated CAPS Camera with IRF-Free Deep Learning Method.Sensors (Basel, Switzerland) · 2025Article
- Rapid Acquisition of High-Pixel Fluorescence Lifetime Images of Living Cells via Image Reconstruction Based on Edge-Preserving Interpolation.Biosensors · 2025Article
- Fluorescence Lifetime Measurements and Analyses: Protocols Using Flow Cytometry and High-Throughput Microscopy.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Simple and Robust Deep Learning Approach for Fast Fluorescence Lifetime Imaging.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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. The 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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Registered trials
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