# Review: Deep Learning in Electron Microscopy

arXiv: https://arxiv.org/abs/2009.08328 · HTML: https://mlsanity.com/paper/2009.08328/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2020.

## Built on by (most cited first, 20 of 20)

- [Ambient Diffusion: Learning Clean Distributions from Corrupted Data](https://mlsanity.com/paper/2305.19256/) (2023)
- [Deep Generative Modeling for Volume Reconstruction in Cryo-Electron Microscopy](https://mlsanity.com/paper/2201.02867/) (2022)
- [Leveraging generative adversarial networks to create realistic scanning transmission electron microscopy images](https://mlsanity.com/paper/2301.07743/) (2023)
- [An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analytics](https://mlsanity.com/paper/2109.14772/) (2021)
- [Disentangling multiple scattering with deep learning: application to strain mapping from electron diffraction patterns](https://mlsanity.com/paper/2202.00204/) (2022)
- [Segmentation in large-scale cellular electron microscopy with deep learning: A literature survey](https://mlsanity.com/paper/2206.07171/) (2022)
- [Data-driven Science and Machine Learning Methods in Laser-Plasma Physics](https://mlsanity.com/paper/2212.00026/) (2022)
- [An End-to-End Computer Vision Methodology for Quantitative Metallography](https://mlsanity.com/paper/2104.11159/) (2021)
- [Design of a Graphical User Interface for Few-Shot Machine Learning Classification of Electron Microscopy Data](https://mlsanity.com/paper/2107.10387/) (2021)
- [Semi-supervised machine learning model for analysis of nanowire morphologies from transmission electron microscopy images](https://mlsanity.com/paper/2203.13875/) (2022)
- [Towards Augmented Microscopy with Reinforcement Learning-Enhanced Workflows](https://mlsanity.com/paper/2208.02865/) (2022)
- [SAIBench: Benchmarking AI for Science](https://mlsanity.com/paper/2206.05418/) (2022)
- [Ensemble of Pre-Trained Neural Networks for Segmentation and Quality Detection of Transmission Electron Microscopy Images](https://mlsanity.com/paper/2209.01908/) (2022)
- [Interpreting deep learning output for out-of-distribution detection](https://mlsanity.com/paper/2211.03637/) (2022)
- [Zero-Shot Image Denoising for High-Resolution Electron Microscopy](https://mlsanity.com/paper/2406.14264/) (2024)
- [Machine learning meets mass spectrometry: a focused perspective](https://mlsanity.com/paper/2407.00117/) (2024)
- [4D-PreNet: A Unified Preprocessing Framework for 4D-STEM Data Analysis](https://mlsanity.com/paper/2508.03775/) (2025)
- [Contrast transfer functions help quantify neural network out-of-distribution generalization in HRTEM](https://mlsanity.com/paper/2512.09067/) (2025)
- [Atomic Depth Estimation From Noisy Electron Microscopy Data Via Deep Learning](https://mlsanity.com/paper/2601.17046/) (2026)
- [Physics Informed Generative AI Enabling Labour Free Segmentation For Microscopy Analysis](https://mlsanity.com/paper/2602.01710/) (2026)

## Abstract (arXiv)

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.