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| Home > Publications database > Deep Neural Networks for Analysis of Microscopy Images—Synthetic Data Generation and Adaptive Sampling > print |
| 001 | 891562 | ||
| 005 | 20230228121558.0 | ||
| 024 | 7 | _ | |a 10.3390/cryst11030258 |2 doi |
| 024 | 7 | _ | |a 2128/33772 |2 Handle |
| 024 | 7 | _ | |a WOS:000633604100001 |2 WOS |
| 037 | _ | _ | |a FZJ-2021-01587 |
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| 100 | 1 | _ | |a Trampert, Patrick |0 P:(DE-HGF)0 |b 0 |
| 245 | _ | _ | |a Deep Neural Networks for Analysis of Microscopy Images—Synthetic Data Generation and Adaptive Sampling |
| 260 | _ | _ | |a Basel |c 2021 |b MDPI |
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| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a 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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| 536 | _ | _ | |a MuDiLingo - A Multiscale Dislocation Language for Data-Driven Materials Science (759419) |0 G:(EU-Grant)759419 |c 759419 |f ERC-2017-STG |x 1 |
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| 700 | 1 | _ | |a Rubinstein, Dmitri |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Boughorbel, Faysal |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Schlinkmann, Christian |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Luschkova, Maria |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Slusallek, Philipp |0 0000-0002-2189-2429 |b 5 |
| 700 | 1 | _ | |a Dahmen, Tim |0 P:(DE-HGF)0 |b 6 |
| 700 | 1 | _ | |a Sandfeld, Stefan |0 P:(DE-Juel1)186075 |b 7 |e Corresponding author |
| 773 | _ | _ | |a 10.3390/cryst11030258 |g Vol. 11, no. 3, p. 258 - |0 PERI:(DE-600)2661516-2 |n 3 |p 258 - |t Crystals |v 11 |y 2021 |x 2073-4352 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/891562/files/Invoice_MDPI_crystals-1104158.pdf |
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| 914 | 1 | _ | |y 2022 |
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