Projects per year
Abstract
Fluorescence lifetime imaging (FLIM) is a powerful tool that provides unique quantitative information for biomedical research. In this study, we propose a multi-layer-perceptron-based mixer (MLP-Mixer) deep learning (DL) algorithm named FLIM-MLP-Mixer for fast and robust FLIM analysis. The FLIM-MLP-Mixer has a simple network architecture yet a powerful learning ability from data. Compared with the traditional fitting and previously reported DL methods, the FLIM-MLP-Mixer shows superior performance in terms of accuracy and calculation speed, which has been validated using both synthetic and experimental data. All results indicate that our proposed method is well suited for accurately estimating lifetime parameters from measured fluorescence histograms, and it has great potential in various real-time FLIM applications.
Original language | English |
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Article number | 7293 |
Number of pages | 10 |
Journal | Sensors |
Volume | 22 |
Issue number | 19 |
DOIs | |
Publication status | Published - 26 Sept 2022 |
Keywords
- fluorescence lifetime imaging (FLIM)
- deep learning
- imaging analysis
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Dive into the research topics of 'Simple and robust deep learning approach for fast fluorescence lifetime imaging'. Together they form a unique fingerprint.Projects
- 1 Finished
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SPRINT: A SuPer-Resolution time-resolved ImagiNg and specTroscopy facility for rapid biomolecular analysis
Li, D. (Principal Investigator), Chamberlain, L. (Co-investigator), Chen, Y. (Co-investigator), Cunningham, M. R. (Co-investigator), Gould, G. (Co-investigator), Hoskisson, P. (Co-investigator), McConnell, G. (Co-investigator), Rattray, Z. (Co-investigator) & Van de Linde, S. (Co-investigator)
BBSRC (Biotech & Biological Sciences Research Council)
1/07/21 → 31/05/23
Project: Research