ArticleJournal of applied crystallography2022
Classification of diffraction patterns using a convolutional neural network in single-particle-imaging experiments performed at X-ray free-electron lasers.
Article in Journal of applied crystallography, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- High-throughput in situ single particle X-ray imaging of dehydrating viral capsids.Light, science & applications · 2026Article
- AI and Robotics Advancement in Analytical Mineral Characterization and Mining Processes: A Review and Research Trends Analysis.Topics in current chemistry (Cham) · 2026Review
- Application of machine learning for nanodiamonds shape and surface classification based on X-ray pattern analysis.Scientific reports · 2025Article
- Neural networks for rapid phase quantification of cultural heritage X-ray powder diffraction data.Journal of applied crystallography · 2024Article
- Introduction to the virtual collection of papers onJournal of applied crystallography · 2024Article
- Application of self-supervised approaches to the classification of X-ray diffraction spectra during phase transitions.Scientific reports · 2023Article
- The structure of tick-borne encephalitis virus determined at X-ray free-electron lasers. Simulations.Journal of synchrotron radiation · 2023Article
- Classification of diffraction patterns using a convolutional neural network in single-particle-imaging experiments performed at X-ray free-electron lasers.Journal of applied crystallography · 2022Article
- Selecting XFEL single-particle snapshots by geometric machine learning.Structural dynamics (Melville, N.Y.) · 2021Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Single particle imaging (SPI) at X-ray free-electron lasers is particularly well suited to determining the 3D structure of particles at room temperature. For a successful reconstruction, diffraction patterns originating from a single hit must be isolated from a large number of acquired patterns. It is proposed that this task could be formulated as an image-classification problem and solved using convolutional neural network (CNN) architectures. Two CNN configurations are developed: one that maximizes the F1 score and one that emphasizes high recall. The CNNs are also combined with expectation-maximization (EM) selection as well as size filtering. It is observed that the CNN selections have lower contrast in power spectral density functions relative to the EM selection used in previous work. However, the reconstruction of the CNN-based selections gives similar results. Introducing CNNs into SPI experiments allows the reconstruction pipeline to be streamlined, enables researchers to classify patterns on the fly, and, as a consequence, enables them to tightly control the duration of their experiments. Incorporating non-standard artificial-intelligence-based solutions into an existing SPI analysis workflow may be beneficial for the future development of SPI experiments.
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