Applying deep learning in automatic and rapid measurement of lattice spacings in HRTEM images
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DOI: 10.1007/s40843-020-1368-7
Abstract: 在图像处理与分析领域, 深度学习发挥着日渐重要的作用. 我们将深度学习应用于高分辨率透射电子显微镜(HRTEM)图像晶 面间距的自动快速测量中, 通过对随机样本数据训练得到具有U型 结构的神经网络, 开发了一种新的图像处理方法. 本方法能够自动 提取快速傅里叶变换(FFT)图像中的衍射斑点, 进一步在计算机视 觉技术的协助下, 可以自动计算与FFT图像中被识别的衍射点相对 应的晶格间距, 并与标准晶体结构数据进行比较. 以Fe3 O4 纳米粒子 的HRTEM图像为例, 用本方法进行自动测量的晶格间距与手动测 量的晶格间距相比, 误差小于1%. 我们的工作证明了深度学习技术 在协助晶体材料发展方面的巨大潜力. read more here.
Keywords: learning automatic; hrtem; deep learning; rapid measurement ... See more keywords
Deep Learning for the Automatic Quantification of Pleural Plaques in Asbestos-Exposed Subjects
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DOI: 10.3390/ijerph19031417
Abstract: Objective: This study aimed to develop and validate an automated artificial intelligence (AI)-driven quantification of pleural plaques in a population of retired workers previously occupationally exposed to asbestos. Methods: CT scans of former workers previously… read more here.
Keywords: automatic quantification; learning automatic; pleural plaques; quantification pleural ... See more keywords