X-ray Scattering Image Classification Using Deep Learning
This work addresses the need for efficient material structure analysis in materials science, but it is incremental as it applies existing deep learning methods to a new domain.
The paper tackled the problem of automatically analyzing x-ray scattering images by applying Convolutional Neural Networks and Convolutional Autoencoders, achieving a 10% improvement over previous methods on synthetic and real datasets.
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.