Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月5日

查看全文
  • Yue Fei,
  • Shuang Fu,
  • Wei Shi,
  • Ke Fang,
  • Ruixiong Wang,
  • Tianlun Zhang,
  • Yiming Li

摘要

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.