Robust and scalable deep learning for X-ray synchrotron image analysis

Nicole Meister, Ziqiao Guan, Jinzhen Wang, Ronald Lashley, Jiliang Liu, Julien Lhermitte, K. Yager, Hong Qin, Bo Sun, Dantong Yu
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引用次数: 6

Abstract

X-ray scattering is a key technique in modern synchrotron facilities towards material analysis and discovery via structural characterization at the molecular scale and nano-scale. Image classification and tagging play a crucial role in recognizing patterns, inferring meaningful physical properties from sample, and guiding subsequent experiment steps. We designed deeplearning based image classification pipelines and gained significant improvements in terms of accuracy and speed. Constrained by available computing resources and optimization library, we need to make trade-off among computation efficiency, input image size and volume, and the flexibility and stability of processing images with different levels of qualities and artifacts. Consequently, our deep learning framework requires careful data preprocessing techniques to down-sample images and extract true image signals. However, X-ray scattering images contain different levels of noise, numerous gaps, rotations, and defects arising from detector limitations, sample (mis)alignment, and experimental configuration. Traditional methods of healing x-ray scattering images make strong assumptions about these artifacts and require hand-crafted procedures and experiment meta-data to de-noise, interpolate measured data to eliminate gaps, and rotate and translate images to align the center of samples with the center of images. These manual procedures are error-prone, experience-driven, and isolated from the intended image prediction, and consequently not scalable to the data rate of X-ray images from modern detectors. We aim to explore deeplearning based image classification techniques that are robust and capable of leverage high-definition experimental images with rich variations even in a production environment that is not defect-free, and ultimately automate labor-intensive data preprocessing tasks and integrate them seamlessly into our TensorFlow based experimental data analysis framework.
用于x射线同步加速器图像分析的鲁棒和可扩展深度学习
x射线散射技术是现代同步加速器设备中通过分子尺度和纳米尺度的结构表征来分析和发现材料的关键技术。图像分类和标记在识别模式、从样本中推断有意义的物理性质以及指导后续实验步骤方面起着至关重要的作用。我们设计了基于深度学习的图像分类管道,在准确率和速度方面都有了显著的提高。在现有计算资源和优化库的限制下,我们需要在计算效率、输入图像大小和体积以及处理不同质量和伪影水平的图像的灵活性和稳定性之间做出权衡。因此,我们的深度学习框架需要仔细的数据预处理技术来降低图像采样并提取真实的图像信号。然而,x射线散射图像包含不同程度的噪声、大量的间隙、旋转和由检测器限制、样品(错误)对准和实验配置引起的缺陷。传统的修复x射线散射图像的方法对这些伪影有很强的假设,需要手工制作的程序和实验元数据来去噪,插值测量数据以消除间隙,旋转和平移图像以使样本中心与图像中心对齐。这些人工程序容易出错,经验驱动,并且与预期的图像预测分离,因此无法扩展到现代探测器x射线图像的数据速率。我们的目标是探索基于深度学习的图像分类技术,这些技术具有鲁棒性,即使在没有缺陷的生产环境中也能够利用具有丰富变化的高清实验图像,并最终自动化劳动密集型数据预处理任务,并将其无缝集成到基于TensorFlow的实验数据分析框架中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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