Name
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.eswa.2021.114740 | EXPERT SYSTEMS WITH APPLICATIONS |
Keywords | DocType | Volume |
X-ray diffraction image, Multi-label classification, Convolutional neural network, Image recognition, Crystallography | Journal | 174 |
ISSN | Citations | PageRank |
0957-4174 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adam Czyzewski | 1 | 0 | 0.34 |
| Faustyna Krawiec | 2 | 0 | 0.34 |
| Dariusz Brzezinski | 3 | 213 | 11.28 |
| Przemyslaw Jerzy Porebski | 4 | 0 | 0.34 |
| Wladek Minor | 5 | 6 | 2.57 |