# DeepPicker: a Deep Learning Approach for Fully Automated Particle Picking in Cryo-EM

arXiv: https://arxiv.org/abs/1605.01838 · HTML: https://mlsanity.com/paper/1605.01838/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2016.

## Built on by (most cited first, 14 of 14)

- [SHREC 2021: Classification in cryo-electron tomograms](https://mlsanity.com/paper/2203.10035/) (2022)
- [DRACO: A Denoising-Reconstruction Autoencoder for Cryo-EM](https://mlsanity.com/paper/2410.11373/) (2024)
- [End-to-End Simultaneous Learning of Single-particle Orientation and 3D Map Reconstruction from Cryo-electron Microscopy Data](https://mlsanity.com/paper/2107.02958/) (2021)
- [Adapting Segment Anything Model (SAM) through Prompt-based Learning for Enhanced Protein Identification in Cryo-EM Micrographs](https://mlsanity.com/paper/2311.16140/) (2023)
- [CryoMAE: Few-Shot Cryo-EM Particle Picking with Masked Autoencoders](https://mlsanity.com/paper/2404.10178/) (2024)
- [Artificial Intelligence Advances for De Novo Molecular Structure Modeling in Cryo-EM](https://mlsanity.com/paper/2102.06125/) (2021)
- [Orthogonal Matrix Retrieval with Spatial Consensus for 3D Unknown-View Tomography](https://mlsanity.com/paper/2207.02985/) (2022)
- [Multi-target detection with rotations](https://mlsanity.com/paper/2101.07709/) (2021)
- [ASOCEM: Automatic Segmentation Of Contaminations in cryo-EM](https://mlsanity.com/paper/2201.06978/) (2022)
- [CRISP: A Framework for Cryo-EM Image Segmentation and Processing with Conditional Random Field](https://mlsanity.com/paper/2502.08287/) (2025)
- [Advanced POD-Based Performance Evaluation of Classifiers Applied to Human Driver Lane Changing Prediction](https://mlsanity.com/paper/2408.15722/) (2024)
- [Joint Denoising of Cryo-EM Projection Images using Polar Transformers](https://mlsanity.com/paper/2506.11283/) (2025)
- [Application of Deep Learning in Biological Data Compression](https://mlsanity.com/paper/2512.12975/) (2025)
- [Reconstruction-Aware Cryo-EM Particle Picking](https://mlsanity.com/paper/2608.28838/) (2026)

## Abstract (arXiv)

Particle picking is a time-consuming step in single-particle analysis and often requires significant interventions from users, which has become a bottleneck for future automated electron cryo-microscopy (cryo-EM). Here we report a deep learning framework, called DeepPicker, to address this problem and fill the current gaps toward a fully automated cryo-EM pipeline. DeepPicker employs a novel cross-molecule training strategy to capture common features of particles from previously-analyzed micrographs, and thus does not require any human intervention during particle picking. Tests on the recently-published cryo-EM data of three complexes have demonstrated that our deep learning based scheme can successfully accomplish the human-level particle picking process and identify a sufficient number of particles that are comparable to those manually by human experts. These results indicate that DeepPicker can provide a practically useful tool to significantly reduce the time and manual effort spent in single-particle analysis and thus greatly facilitate high-resolution cryo-EM structure determination.