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

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 d...

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Bibliographic Details

Published in:	Crystals
Main Authors:	Patrick Trampert, Dmitri Rubinstein, Faysal Boughorbel, Christian Schlinkmann, Maria Luschkova, Philipp Slusallek, Tim Dahmen, Stefan Sandfeld
Format:	Article
Language:	English
Published:	MDPI AG 2021-03-01
Subjects:	microscopy image segmentation deep learning data augmentation synthetic training data parametric models
Online Access:	https://www.mdpi.com/2073-4352/11/3/258