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Hardware Inspired Neural Network for Efficient Time-Resolved Biomedical Imaging.

Zhenya Zang, Dong Xiao, Quan Wang

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |September 10, 2022
    PubMed
    Summary
    This summary is machine-generated.

    We developed a lightweight, quantized neural network for fast fluorescence lifetime imaging (FLIM). This efficient model achieves high accuracy for early cancer diagnosis without performance loss, enabling real-time processing on embedded systems.

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    Area of Science:

    • Biomedical Optics
    • Artificial Intelligence
    • Medical Imaging

    Background:

    • Convolutional neural networks (CNNs) show promise for fluorescence lifetime imaging (FLIM).
    • Complex CNN architectures hinder real-time processing on embedded hardware.
    • Efficient and lightweight models are needed for practical FLIM applications.

    Purpose of the Study:

    • To develop a lightweight, quantized neural network for fast FLIM.
    • To enable real-time FLIM processing on embedded systems.
    • To validate the network's performance on synthetic and real-world biological data.

    Main Methods:

    • Implemented a quantized neural architecture simplifying forward propagation via additions instead of matrix multiplications.
    • Utilized low bit-width data quantization.
    • Validated using synthetic 3-D lifetime data and human prostatic cancer cells with gold nanoprobes.

    Main Results:

    • Achieved a 37.8% compression ratio with no performance degradation.
    • Demonstrated accurate average lifetime calculations on synthetic data.
    • Successfully validated the network's feasibility on real-world cancer cell data.

    Conclusions:

    • The lightweight, quantized neural network enables fast FLIM imaging.
    • This approach facilitates non-invasive, early cancer diagnosis through fluorescence lifetime analysis.
    • The network accelerates diagnostic processes, making advanced imaging accessible to clinicians.