Materials discovery: Fine-grained classification of X-ray scattering images

Abstract

We explore the use of computer vision methods for organizing, searching, and classifying x-ray scattering images. X-ray scattering is a technique that shines an intense beam of x-rays through a sample of interest. By recording the intensity of x-ray deflection as a function of angle, scientists can measure the structure of materials at the molecular and nano-scale. Current and planned synchrotron instruments are producing x-ray scattering data at an unprecedented rate, making the design of automatic analysis techniques crucial for future research. In this paper, we devise an attribute-based approach to recognition in x-ray scattering images and demonstrate applications to image annotation and retrieval.

X-Ray Scattering Image Classification Using Deep Learning

“…To the best of our knowledge, there exists little work for automatically analyzing x-ray scattering images. The most closely-related prior work is that of Kiapour et al [6], which also aimed to recognize the set of image attributes consid-ered in this paper. Their work used hand-designed features such as HOG [1] and SIFT [13].…”

“…Unfortunately, these types of features were designed for natural images instead of xray images. As a consequence, the method developed by Kiapour et al [6] lacks the performance that would be desired for trustworthy automated analysis of real scientific data.…”

“…However, the size of the previously-available x-ray image dataset is quite small; far too small for robust application of deep learning methods. The dataset collected by [6] only contains 2832 images. Manually labeling x-ray images requires a significant amount of domain experts' time, which could otherwise be spent on high-level scientific discovery.…”

“…The trained networks can be used to extract feature representations for both synthetic and real x-ray scattering images. Using this type of feature representation, we obtained a method that outperforms hand-crafted features [6] by 10% on both synthetic and real datasets.…”

“…X-ray Materials Discovery Dataset (XMD) [6] contains 2832 x-ray scattering images collected from thirteen x-ray scattering measurement runs. The number of images in each experiment varies between 54 and 618.…”