Classification of diffraction patterns in single particle imaging experiments performed at x-ray free-electron lasers using a convolutional neural network

Abstract

Single particle imaging (SPI) is a promising method of native structure determination, which has undergone fast progress with the development of x-ray free-electron lasers. Large amounts of data are collected during SPI experiments, driving the need for automated data analysis. The necessary data analysis pipeline has a number of steps including binary object classification (single versus non-single hits). Classification and object detection are areas where deep neural networks currently outperform other approaches. In this work, we use the fast object detector networks YOLOv2 and YOLOv3. By exploiting transfer learning, a moderate amount of data is sufficient to train the neural network. We demonstrate here that a convolutional neural network can be successfully used to classify data from SPI experiments. We compare the results of classification for the two different networks, with different depth and architecture, by applying them to the same SPI data with different data representation. The best results are obtained for diffracted intensity represented by color images on a linear scale using YOLOv2 for classification. It shows an accuracy of about 95% with precision and recall of about 50% and 60%, respectively, in comparison to manual data classification.

Classification of diffraction patterns using a convolutional neural network in single-particle-imaging experiments performed at X-ray free-electron lasers

“…Thus, it is unsurprising that CNN-based solutions have been recently successfully applied in our domain: specifically, the classification of diffraction patterns in tomography experiments at synchrotron sources (Yang et al, 2020) and in coherent diffraction imaging experiments at synchrotron facilities Wu, Juhas et al, 2021) and at XFELs (Shi et al, 2019;Zimmermann et al, 2019). As we showed in our previous work (Ignatenko et al, 2021), a CNN-based solution can be successfully applied to the single-hit diffraction pattern classification step (Fig. 1, blue arrows).…”

Skopi: a simulation package for diffractive imaging of noncrystalline biomolecules

“…This may prove particularly valuable for the development of machine learning algorithms to perform classification, since labeled data can easily be generated in bulk. Classification algorithms would benefit data pre-processing to separate single-particle hits from aggregate shots and better assess hit rates, and could also be used during reconstruction to sort diffraction images based on the particle's conformation (Ignatenko et al, 2021), as done in cryoelectron microscopy (Punjani & Fleet, 2021;Zhong et al, 2021;Chen & Ludtke, 2021).…”

Skopi: a simulation package for diffractive imaging of noncrystalline biomolecules

“…This may prove particularly valuable for the development of machine learning algorithms to perform classification, since labeled data can easily be generated in bulk. Classification algorithms would not only benefit data pre-processing to separate single-particle hits from aggregate shots, but could also be used during reconstruction to sort diffraction images based on the particle’s conformation (Ignatenko et al ., 2021), as done in cryo-electron microscopy (Punjani & Fleet, 2021; Zhong et al ., 2021; Chen & Ludtke, 2021).…”