Review: Deep Learning in Electron Microscopy

Abstract

Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Afterwards, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy.

arXiv comment33 pages, 16 figures + 2 tables + 65 pages of references

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Cited by in corpus (13)

• Segmentation in large-scale cellular electron microscopy with deep learning: A literature survey
• Leveraging generative adversarial networks to create realistic scanning transmission electron microscopy images
• Developing and Evaluating Deep Neural Network-based Denoising for Nanoparticle TEM Images with Ultra-low Signal-to-Noise
• An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analytics
• Design of a Graphical User Interface for Few-Shot Machine Learning Classification of Electron Microscopy Data
• Towards Augmented Microscopy with Reinforcement Learning-Enhanced Workflows
• Evaluating Stage Motion for Automated Electron Microscopy
• Direct visualization of defect-controlled diffusion in van der Waals gaps
• Zero-Shot Image Denoising for High-Resolution Electron Microscopy
• SAIBench: Benchmarking AI for Science
• Image Difference Metrics for High-Resolution Electron Microscopy
• Morphomics via Next-generation Electron Microscopy
• Lost Data in Electron Microscopy