Trampert, P.; Rubinstein, D.; Boughorbel, F.; Schlinkmann, C.; Luschkova, M.; Slusallek, P.; Dahmen, T.; Sandfeld, S. Deep Neural Networks for Analysis of Microscopy Images—Synthetic Data Generation and Adaptive Sampling. Crystals 2021, 11, 258, doi:10.3390/cryst11030258.
Trampert, P.; Rubinstein, D.; Boughorbel, F.; Schlinkmann, C.; Luschkova, M.; Slusallek, P.; Dahmen, T.; Sandfeld, S. Deep Neural Networks for Analysis of Microscopy Images—Synthetic Data Generation and Adaptive Sampling. Crystals 2021, 11, 258, doi:10.3390/cryst11030258.
Trampert, P.; Rubinstein, D.; Boughorbel, F.; Schlinkmann, C.; Luschkova, M.; Slusallek, P.; Dahmen, T.; Sandfeld, S. Deep Neural Networks for Analysis of Microscopy Images—Synthetic Data Generation and Adaptive Sampling. Crystals 2021, 11, 258, doi:10.3390/cryst11030258.
Trampert, P.; Rubinstein, D.; Boughorbel, F.; Schlinkmann, C.; Luschkova, M.; Slusallek, P.; Dahmen, T.; Sandfeld, S. Deep Neural Networks for Analysis of Microscopy Images—Synthetic Data Generation and Adaptive Sampling. Crystals 2021, 11, 258, doi:10.3390/cryst11030258.
Abstract
The analysis of microscopy images has always been an important yet time consuming process in 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.
Keywords
Microscopy Image Segmentation; Deep Learning; Data Augmentation; Synthetic Training Data; Parametric Models
Subject
Chemistry and Materials Science, Biomaterials
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
