Abstracts Book - IMRC 2018

• SD2-O007
DEEP CONVOLUTIONAL NEURAL NETWORKS FOR AUTOMATED
CLASSIFICATION AND FEATURE EXTRACTION COHERENT X-RAY
DIFFRACTION IMAGING DATA
Mathew Cherukara 1 , Youssef Nashed 2 , Ross Harder 1
1 Argonne National Laboratory, Advanced Photon Source, United States. 2 Argonne National
Laboratory, Math and Computer Science, United States.
Coherent X-ray diffraction imaging (CDI) is a powerful technique for operando
characterization. Visualizing defects, dynamics, and structural evolution using
CDI, however, remains a grand challenge since state-of-the-art iterative
reconstruction algorithms for CDI data are time-consuming and
computationally expensive, which precludes real-time feedback. Such
computational challenges associated with image reconstruction are forecast to
become even more acute following the APS-Upgrade (APS-U), when the
resolution of the acquired data and consequently the size of the data sets will
be ~100X larger. Furthermore, the reconstruction algorithms require human
inputs to guide their convergence, which is a very subjective process. The need
of the hour is an automated workflow that would enable real-time feature
detection the raw X-ray diffraction data. Convolutional neural networks (CNNs)
have shown great promise in many image classification tasks. The key to the
success of CNNs stems that fact that deep learning architectures can learn
complex features required for pattern recognition in images without the need
for hand-designed features.
In this talk, I will show results our recent work where we have trained a CNN to
classify and identify the defect structure of raw X-ray diffraction data without the
need for any reconstruction through iterative phase retrieval. While the CNN
was trained on atomistic data, it performs at ~95% accuracy on real-world X-ray
diffraction data, indicating the robustness of the network.
Keywords: Convolutional Neural Network, X-ray diffraction imaging, Deep learning
Presenting authors email: mcherukara@anl.gov