Deep neural networks for classifying complex features in diffraction images

Abstract

Intense short-wavelength pulses from free-electron lasers and high-harmonic-generation sources enable diffractive imaging of individual nano-sized objects with a single x-ray laser shot. The enormous data sets with up to several million diffraction patterns represent a severe problem for data analysis, due to 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 facing 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 in [Langbehn et al. Phys. Rev. Lett. 121, 255301 (2018)] the first 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 benchmarking. 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.

Super-resolution of real-world rock microcomputed tomography images using cycle-consistent generative adversarial networks

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Unsupervised learning approaches to characterizing heterogeneous samples using X-ray single-particle imaging

“…In past years, different machine learning algorithms have been developed for use in this classification of structural variability. These algorithms are usually applied to the patterns themselves using methods including spectral clustering (Yoon et al, 2011), support vector machines (Bobkov et al, 2015) and convolutional neural networks (Zimmermann et al, 2019;Ignatenko et al, 2021).…”

Noise reduction and mask removal neural network for X-ray single-particle imaging

“…X-ray detectors can generate up to 15 Gbyte s À1 of raw data (Muennich et al, 2016) and machine learning solutions may be helpful to improve and speed up data analysis. Up to now, only a few studies have addressed this potential directly for X-ray SPI experiments, employing neural networks for image classification (Langbehn et al, 2018;Shi et al, 2019;Zimmermann et al, 2019;Ignatenko et al, 2021), for defect identification and phase retrieval (Cherukara et al, 2018;Lim et al, 2021;Wu, Juhas et al, 2021;, for shape and orientation recovery of silver nanoclusters (Stielow et al, 2020), and for the reconstruction of electron densities of metallic nanoparticles from experimental data of the 3D Fourier space (Chan et al, 2020).…”