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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主要な著者:	Patrick Trampert, Dmitri Rubinstein, Faysal Boughorbel, Christian Schlinkmann, Maria Luschkova, Philipp Slusallek, Tim Dahmen, Stefan Sandfeld
フォーマット:	Artigo
言語:	Inglês
出版事項:	MDPI AG 2021-03-01
シリーズ:	Crystals
主題:	microscopy image segmentation deep learning data augmentation synthetic training data parametric models
オンライン･アクセス:	https://www.mdpi.com/2073-4352/11/3/258
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