Scalable and lightweight deep learning for efficient high accuracy single-molecule localization microscopy
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| Publicado en: | Nature Communications vol. 16, no. 1 (2025), p. 7217-7226 |
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| Outros autores: | , , , , , |
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| Acceso en liña: | Citation/Abstract Full Text Full Text - PDF |
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| 022 | |a 2041-1723 | ||
| 024 | 7 | |a 10.1038/s41467-025-62662-5 |2 doi | |
| 035 | |a 3236797132 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 145839 |2 nlm | ||
| 100 | 1 | |a Fei, Yue |u Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China (ROR: https://ror.org/049tv2d57) (GRID: grid.263817.9) (ISNI: 0000 0004 1773 1790) | |
| 245 | 1 | |a Scalable and lightweight deep learning for efficient high accuracy single-molecule localization microscopy | |
| 260 | |b Nature Publishing Group |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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.This study presents LiteLoc, a lightweight and scalable AI model for efficient and accurate single molecule localization microscopy data analysis, bringing real-time deep-learning-based analysis to the era of high throughput super resolution imaging. | |
| 653 | |a Microscopy | ||
| 653 | |a Parallel processing | ||
| 653 | |a Central processing units--CPUs | ||
| 653 | |a Accuracy | ||
| 653 | |a Deep learning | ||
| 653 | |a Data analysis | ||
| 653 | |a Biological research | ||
| 653 | |a Localization | ||
| 653 | |a Machine learning | ||
| 653 | |a Performance evaluation | ||
| 653 | |a Efficiency | ||
| 653 | |a Energy consumption | ||
| 653 | |a Neural networks | ||
| 653 | |a Graphics processing units | ||
| 653 | |a Network latency | ||
| 653 | |a Algorithms | ||
| 653 | |a Latency | ||
| 653 | |a Real time | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Fu, Shuang |u Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China (ROR: https://ror.org/049tv2d57) (GRID: grid.263817.9) (ISNI: 0000 0004 1773 1790) | |
| 700 | 1 | |a Shi, Wei |u Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China (ROR: https://ror.org/049tv2d57) (GRID: grid.263817.9) (ISNI: 0000 0004 1773 1790); School of Life Sciences, Southern University of Science and Technology, Shenzhen, China (ROR: https://ror.org/049tv2d57) (GRID: grid.263817.9) (ISNI: 0000 0004 1773 1790) | |
| 700 | 1 | |a Fang, Ke |u Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China (ROR: https://ror.org/049tv2d57) (GRID: grid.263817.9) (ISNI: 0000 0004 1773 1790) | |
| 700 | 1 | |a Wang, Ruixiong |u Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China (ROR: https://ror.org/049tv2d57) (GRID: grid.263817.9) (ISNI: 0000 0004 1773 1790) | |
| 700 | 1 | |a Zhang, Tianlun |u Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China (ROR: https://ror.org/049tv2d57) (GRID: grid.263817.9) (ISNI: 0000 0004 1773 1790) | |
| 700 | 1 | |a Li, Yiming |u Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China (ROR: https://ror.org/049tv2d57) (GRID: grid.263817.9) (ISNI: 0000 0004 1773 1790); Guangdong Provincial Key Laboratory of Advanced Biomaterials, Southern University of Science and Technology, Shenzhen, China (ROR: https://ror.org/049tv2d57) (GRID: grid.263817.9) (ISNI: 0000 0004 1773 1790) | |
| 773 | 0 | |t Nature Communications |g vol. 16, no. 1 (2025), p. 7217-7226 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3236797132/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
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| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3236797132/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |