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Deep learning in single-molecule microscopy: fundamentals, caveats, and recent developments [Invited].

Leonhard Möckl1, Anish R Roy1, W E Moerner1

  • 1Department of Chemistry, Stanford University, Stanford, CA 94305, USA.

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Deep learning is revolutionizing single-molecule imaging analysis. This review covers its history, core concepts, and essential data practices for accurate results in this scientific field.

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Area of Science:

  • * Single-molecule imaging and analysis.
  • * Application of artificial intelligence in scientific research.

Background:

  • * Deep learning methods have seen widespread adoption across scientific disciplines.
  • * Recent advancements have extended deep learning applications to the single-molecule community.

Purpose of the Study:

  • * To review significant contributions of deep learning in single-molecule imaging.
  • * To provide historical context for modern deep learning development.
  • * To summarize fundamental deep learning concepts.

Main Methods:

  • * Literature review of deep learning applications in single-molecule imaging.
  • * Explanation of historical milestones in deep learning.
  • * Summary of core deep learning principles.

Main Results:

  • * Deep learning is increasingly vital for analyzing complex single-molecule imaging data.
  • * Understanding deep learning fundamentals is crucial for researchers.
  • * Proper data composition is highlighted as essential for reliable outcomes.

Conclusions:

  • * Deep learning offers powerful tools for advancing single-molecule imaging.
  • * A foundational understanding of deep learning and data quality is necessary for its effective application.
  • * This review serves as a guide for leveraging deep learning in this specialized scientific area.