Crystals (Mar 2021)

Deep Neural Networks for Analysis of Microscopy Images—Synthetic Data Generation and Adaptive Sampling

  • Patrick Trampert,
  • Dmitri Rubinstein,
  • Faysal Boughorbel,
  • Christian Schlinkmann,
  • Maria Luschkova,
  • Philipp Slusallek,
  • Tim Dahmen,
  • Stefan Sandfeld

DOI
https://doi.org/10.3390/cryst11030258
Journal volume & issue
Vol. 11, no. 3
p. 258

Abstract

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The analysis of microscopy images has always been an important yet time consuming process in materials science. Convolutional Neural Networks (CNNs) have been very successfully used for a number of tasks, such as image segmentation. However, training a CNN requires a large amount of hand annotated data, which can be a problem for material science data. We present a procedure to generate synthetic data based on ad hoc parametric data modelling for enhancing generalization of trained neural network models. Especially for situations where it is not possible to gather a lot of data, such an approach is beneficial and may enable to train a neural network reasonably. Furthermore, we show that targeted data generation by adaptively sampling the parameter space of the generative models gives superior results compared to generating random data points.

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