Automatic X-ray scattering image annotation via double-view Fourier-Bessel convolutional networks

X-ray scattering is a key technique towards material analysis and discovery. Modern x-ray facilities are producing x-ray scattering images at such an unprecedented rate that machine aided intelligent analysis is required for scientific discovery. This paper articulates a novel physics-aware image feature transform, Fourier-Bessel transform (FBT), in conjunction with deep representation learning, to tackle the problem of annotating x-ray scattering images with a diverse label set of physics characteristics. We devise a novel joint inference model, Double-View Fourier-Bessel Convolutional Neural Network (DVFB-CNN) to integrate feature learning in both polar frequency and image domains. For polar frequency analysis, we develop an FBT estimation algorithm for partially observed x-ray images, and train a dedicated CNN to extract structural information from FBT. We demonstrate that our deep Fourier-Bessel features well complement standard convolutional features, and the joint network (i.e., DVFB-CNN) improves mean average precision by 13% in multilabel annotation. We also conduct transfer learning on real experimental datasets to further confirm that our joint model is well generalizable.