Applications of deep learning in electron microscopy.

Area of Science:

• Materials Science
• Biophysics
• Chemistry

Background:

• Electron microscopy (EM) generates massive datasets due to advanced detectors.
• Manual data processing is becoming infeasible for these large datasets.
• Machine learning (ML) offers automated solutions for EM data analysis.

Purpose of the Study:

• To review the expanding application of ML in electron microscopy.
• To summarize ML network architectures and error metrics used in EM.
• To highlight adaptations of ML for specific EM challenges in physical and life sciences.

Main Methods:

• Review of existing literature on ML applications in EM.
• Categorization of ML network architectures (e.g., convolutional neural networks).
• Analysis of error metrics relevant to EM data processing tasks.

Main Results:

• Identified key ML techniques applied to EM problems such as denoising and inpainting.
• Detailed the modifications made to conventional ML networks and training data for EM.
• Showcased successful ML implementations across physical and life science domains.

Conclusions:

• ML is crucial for handling the scale of modern EM data.
• Tailored ML approaches are effective for specific EM data processing tasks.
• The integration of ML is advancing research in both physical and life sciences using EM.