X-ray Scattering Image Classification Using Deep Learning

2016/11/10 by Boyu Wang, Wang, Boyu, Kevin G. Yager +5
Materials Science · Medicine · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning in Materials Science #Medical Imaging Techniques and Applications #Radiomics and Machine Learning in Medical Imaging

paper · pdf · doi:10.48550/arxiv.1611.03313

openalex publication_date 2016/11/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Visual inspection of x-ray scattering images is a powerful technique for probing the physical structure of materials at the molecular scale. In this paper, we explore the use of deep learning to develop methods for automatically analyzing x-ray scattering images. In particular, we apply Convolutional Neural Networks and Convolutional Autoencoders for x-ray scattering image classification. To acquire enough training data for deep learning, we use simulation software to generate synthetic x-ray scattering images. Experiments show that deep learning methods outperform previously published methods by 10% on synthetic and real datasets.

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