Deep neural networks for classifying complex features in diffraction images.-猫眼课题宝

DOI：

10.1103/physreve.99.063309

发表时间：

2019-03

期刊：

Physical review. E

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Julian Zimmermann;B. Langbehn;R. Cucini;M. Di Fraia;P. Finetti;A. LaForge;T. Nishiyama;Y. Ovcharenko;P. Piseri;O. Plekan;K. Prince;F. Stienkemeier;K. Ueda;C. Callegari;T. Möller;D. Rupp

来自自由电子激光器和高谐波源的强短波脉冲使得单个纳米级物体的衍射成像成为可能。由于成像数据的高维性，数以百万计的衍射图案的庞大数据集给数据分析带来了严重的问题。特征识别和选择是降维的关键步骤。通常，定制算法是在相当大的努力下开发出来的，以近似与单个标本相关的特定特征，但由于它们面临不同的实验条件，这些方法不能很好地推广。另一方面，深度神经网络是当今自动图像识别革命的主要工具，这一发展尚未充分发挥其在科学数据分析方面的潜力。我们最近发表了[Langbehn et al., Phys.]基于深度神经网络的氦纳米液滴广角衍射图像特征提取方法[j] .光子学报，2016,36(5):591 - 591。本文介绍了用于衍射图像分类和系统基准测试的深度神经网络的建立、改进和训练过程。我们发现深度神经网络在复杂衍射模式的分类和分类方面明显优于以往的尝试，并且在大量实验相干衍射成像数据的后处理过程中提供了急需的帮助。

Intense short-wavelength pulses from free-electron lasers and high-harmonic-generation sources enable diffractive imaging of individual nanosized objects with a single x-ray laser shot. The enormous data sets with up to several million diffraction patterns present a severe problem for data analysis because of the high dimensionality of imaging data. Feature recognition and selection is a crucial step to reduce the dimensionality. Usually, custom-made algorithms are developed at a considerable effort to approximate the particular features connected to an individual specimen, but because they face different experimental conditions, these approaches do not generalize well. On the other hand, deep neural networks are the principal instrument for today's revolution in automated image recognition, a development that has not been adapted to its full potential for data analysis in science. We recently published [Langbehn et al., Phys. Rev. Lett. 121, 255301 (2018)PRLTAO0031-900710.1103/PhysRevLett.121.255301] the application of a deep neural network as a feature extractor for wide-angle diffraction images of helium nanodroplets. Here we present the setup, our modifications, and the training process of the deep neural network for diffraction image classification and its systematic bench marking. We find that deep neural networks significantly outperform previous attempts for sorting and classifying complex diffraction patterns and are a significant improvement for the much-needed assistance during postprocessing of large amounts of experimental coherent diffraction imaging data.

Deep neural networks for classifying complex features in diffraction images.

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