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Scalable and lightweight deep learning for efficient high accuracy single-molecule localization microscopy

Author

Listed:
  • Yue Fei

    (Southern University of Science and Technology)

  • Shuang Fu

    (Southern University of Science and Technology)

  • Wei Shi

    (Southern University of Science and Technology
    Southern University of Science and Technology)

  • Ke Fang

    (Southern University of Science and Technology)

  • Ruixiong Wang

    (Southern University of Science and Technology)

  • Tianlun Zhang

    (Southern University of Science and Technology)

  • Yiming Li

    (Southern University of Science and Technology
    Southern University of Science and Technology)

Abstract

Deep learning has significantly improved the performance of single-molecule localization microscopy (SMLM), but many existing methods remain computationally intensive, limiting their applicability in high-throughput settings. To address these challenges, we present LiteLoc, a scalable analysis framework for high-throughput SMLM data analysis. LiteLoc employs a lightweight neural network architecture and integrates parallel processing across central processing unit (CPU) and graphics processing unit (GPU) resources to reduce latency and energy consumption without sacrificing localization accuracy. LiteLoc demonstrates substantial gains in processing speed and resource efficiency, making it an effective and scalable tool for routine SMLM workflows in biological research.

Suggested Citation

  • Yue Fei & Shuang Fu & Wei Shi & Ke Fang & Ruixiong Wang & Tianlun Zhang & Yiming Li, 2025. "Scalable and lightweight deep learning for efficient high accuracy single-molecule localization microscopy," Nature Communications, Nature, vol. 16(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-62662-5
    DOI: 10.1038/s41467-025-62662-5
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