Deep Learning, AI employed to further X-ray Data Technology

Introduction

 

Deep learning falls under the category of machine learning in artificial intelligence which has networks capable of learning unsupervised from data that is unstructured or unlabeled. It is also known as deep neural learning, or deep neural network. Unsupervised learning refers to the use of artificial intelligence algorithms to detect patterns in data sets. These data sets contain data points which are not classified at all.

 

 

Transformation

 

Scientists from the United States Department of Energy’s (DOE) Argonne National Laboratory employ deep learning and artificial intelligence processes and its various methods in order to enhance APS i.e. Advanced Photon Source and at the same time, envision X-ray data in a three dimensional order.

 

A computational network has been established which is known as 3D-CDI-NN. And this structure elaborated that 3D dimensional visualizations can be created and extracted from the data that have been collected before at the APS. It is comparatively faster than all other traditional methods of creating and visualising.

 

Coherent diffraction imaging (CDI) is a “lensless” technique for 2D as well as 3D reconstruction of the image of nanoscale structures such as nanotubes, nanocrystals, porous nanocrystalline layers, defects,and more. When a highly coherent beam is incident on an object, a diffraction pattern is produced and then, it is collected by a detector and is transformed into images.

 

 (Must Check:Introduction to Machine Learning: Supervised, Unsupervised and Reinforcement Learning))

 

According to HealthITAnalytics, the leader of the Computational X-ray Science Division (XSD), Mathew Cherukara says that the current detectors only capture some of the beam.

 

Scientists are mainly dependent on computers to fill in the missing data, but the problem lies in time management because it slows down the process. According to Cherukara, it would be easier if AI is assisted and trained to identify objects that undergo a transformation from raw data. In that case, scientists won’t have to go looking for missing information. Lead author Henry Chan remarks:

 

“We used computer simulations to create crystals of different shapes and sizes, and we converted them into images and diffraction patterns for the neural network to learn.” And in this way, the process might be able to generate “many realistic crystals for training”. There will be enough room for one to make amendments with respect to its improvement and efficiency.

 

After the execution of this testing process, scientists have found that networks can construct images  with less data. The team stated that they would have to collect more data and reinvent data analytics. Cherukara stated that their “current methods are not enough to keep up. Machine learning can make full use and go beyond what is currently possible.”