Machine learning for faster and smarter fluorescence lifetime imaging microscopy

@article{Mannam2020MachineLF,
  title={Machine learning for faster and smarter fluorescence lifetime imaging microscopy},
  author={Varun Mannam and Yide Zhang and Xiaotong Yuan and Cara Ravasio and Scott S. Howard},
  journal={Journal of Physics: Photonics},
  year={2020},
  volume={2}
}
Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique in biomedical research that uses the fluorophore decay rate to provide additional contrast in fluorescence microscopy. However, at present, the calculation, analysis, and interpretation of FLIM is a complex, slow, and computationally expensive process. Machine learning (ML) techniques are well suited to extract and interpret measurements from multi-dimensional FLIM data sets with substantial improvement in speed over… 

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