用卷积神经网络对在X射线自由电子激光器上进行的单粒子成像实验中的衍射图样进行分类
Classification of diffraction patterns in single particle imaging experiments performed at X-ray free-electron lasers using a convolutional neural network
摘要：
单粒子成像（SPI）是一种很有前途的天然结构测定方法，随着X射线自由电子激光的发展而得到了迅速的发展。SPI实验期间收集了大量数据，推动了对自动数据分析的需求。必要的数据分析管道有许多步骤，包括二进制对象分类（单次与多次命中）。分类和对象检测是深度神经网络目前优于其他方法的领域。在这项工作中，我们使用快速对象检测器网络YOLOv2和YOLOv3。通过利用迁移学习，适量的数据足以用于神经网络的训练。我们在这里证明了卷积神经网络（CNN）可以成功地用于对SPI实验的数据进行分类。我们比较了两种不同网络的分类结果，具有不同的深度和架构，通过将它们应用于具有不同数据表示的相同SPI数据。YOLOv2彩色图像线性尺度分类取得了最好的结果，它显示了约97%的准确率与约52%和61%的召回率，分别与人工数据分类。
英文摘要：
Single particle imaging (SPI) is a promising method for native structure determination which has undergone a 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 multiple 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 for training of the neural network. We demonstrate here that a convolutional neural network (CNN) 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 YOLOv2 color images linear scale classification, which shows an accuracy of about 97% with the precision and recall of about 52% and 61%, respectively, which is in comparison to manual data classification.
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