ArticleExpert systems with applications2021
Detecting anomalies in X-ray diffraction images using convolutional neural networks.
Article in Expert systems with applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Artifact segmentation using the U-Net architecture for powder X-ray diffraction images.Journal of synchrotron radiation · 2026Article
- Advancing Battery Manufacturing: Synchrotron Characterization for Industry.Chemical reviews · 2026Review
- Ligand identification in CryoEM and X-ray maps using deep learning.Bioinformatics (Oxford, England) · 2024Article
- Ligand Identification in CryoEM and X-ray Maps Using Deep Learning.bioRxiv : the preprint server for biology · 2024Article
- Data reduction for X-ray serial crystallography using machine learning.Journal of applied crystallography · 2023Article
- Robust Estimation of the Chronological Age of Children and Adolescents Using Tooth Geometry Indicators and POD-GP.International journal of environmental research and public health · 2022Article
- Detecting ice artefacts in processed macromolecular diffraction data with machine learning.Acta crystallographica. Section D, Structural biology · 2022Article
- Tooth and Bone Parameters in the Assessment of the Chronological Age of Children and Adolescents Using Neural Modelling Methods.Sensors (Basel, Switzerland) · 2021Article
- Detecting anomalies in X-ray diffraction images using convolutional neural networks.Expert systems with applications · 2021Article
- The current role and evolution of X-ray crystallography in drug discovery and development.Expert opinion on drug discoveryReview
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Our understanding of life is based upon the interpretation of macromolecular structures and their dynamics. Almost 90% of currently known macromolecular models originated from electron density maps constructed using X-ray diffraction images. Even though diffraction images are critical for structure determination, due to their vast amounts and noisy, non-intuitive nature, their quality is rarely inspected. In this paper, we use recent advances in machine learning to automatically detect seven types of anomalies in X-ray diffraction images. For this purpose, we utilize a novel X-ray beam center detection algorithm, propose three different image representations, and compare the predictive performance of general-purpose classifiers and deep convolutional neural networks (CNNs). In benchmark tests on a set of 6,311 X-ray diffraction images, the proposed CNN achieved between 87% and 99% accuracy depending on the type of anomaly. Experimental results show that the proposed anomaly detection system can be considered suitable for early detection of sub-optimal data collection conditions and malfunctions at X-ray experimental stations.
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Registered trials
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