Use of Deep Learning for structural analysis of CT-images of soil samples

Soil samples from several European countries were scanned using medical
computer tomography (CT) device and are now available as CT images. The
analysis of these samples was carried out using deep learning methods. For
this purpose, a VGG16 network was trained with the CT-images (X). For the
annotation (y) a new method for automated annotation,
"surrogate'' learning, was introduced. The generated neural
networks (NN) were subjected to a detailed analysis. Among other things,
transfer learning was used to check whether the NN can also be trained to
other y-values. Visually, the NN was verified using a gradient-based class
activation mapping (grad-CAM) algorithm. These analyses showed that the NN
was able to generalize, i.e. to capture the spatial structure of the soil
sample. Possible applications of the models are discussed.