Deep learning-based diatom taxonomy on virtual slides
Kloster M, Langenkämper D, Zurowietz M, Beszteri B, Nattkemper TW (2020)
Scientific Reports 10(1): 14416.
Zeitschriftenaufsatz
| Veröffentlicht | Englisch
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s41598-020-71165-w.pdf
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Autor*in
Kloster, MichaelUniBi;
Langenkämper, DanielUniBi ;
Zurowietz, MartinUniBi ;
Beszteri, Bánk;
Nattkemper, Tim WilhelmUniBi
Abstract / Bemerkung
Deep convolutional neural networks are emerging as the state of the art method for supervised classification of images also in the context of taxonomic identification. Different morphologies and imaging technologies applied across organismal groups lead to highly specific image domains, which need customization of deep learning solutions. Here we provide an example using deep convolutional neural networks (CNNs) for taxonomic identification of the morphologically diverse microalgal group of diatoms. Using a combination of high-resolution slide scanning microscopy, web-based collaborative image annotation and diatom-tailored image analysis, we assembled a diatom image database from two Southern Ocean expeditions. We use these data to investigate the effect of CNN architecture, background masking, data set size and possible concept drift upon image classification performance. Surprisingly, VGG16, a relatively old network architecture, showed the best performance and generalizing ability on our images. Different from a previous study, we found that background masking slightly improved performance. In general, training only a classifier on top of convolutional layers pre-trained on extensive, but not domain-specific image data showed surprisingly high performance (F1 scores around 97%) with already relatively few (100–300) examples per class, indicating that domain adaptation to a novel taxonomic group can be feasible with a limited investment of effort.
Stichworte
Multidisciplinary
Erscheinungsjahr
2020
Zeitschriftentitel
Scientific Reports
Band
10
Ausgabe
1
Art.-Nr.
14416
Urheberrecht / Lizenzen
eISSN
2045-2322
Page URI
https://pub.uni-bielefeld.de/record/2945723
Zitieren
Kloster M, Langenkämper D, Zurowietz M, Beszteri B, Nattkemper TW. Deep learning-based diatom taxonomy on virtual slides. Scientific Reports. 2020;10(1): 14416.
Kloster, M., Langenkämper, D., Zurowietz, M., Beszteri, B., & Nattkemper, T. W. (2020). Deep learning-based diatom taxonomy on virtual slides. Scientific Reports, 10(1), 14416. https://doi.org/10.1038/s41598-020-71165-w
Kloster, Michael, Langenkämper, Daniel, Zurowietz, Martin, Beszteri, Bánk, and Nattkemper, Tim Wilhelm. 2020. “Deep learning-based diatom taxonomy on virtual slides”. Scientific Reports 10 (1): 14416.
Kloster, M., Langenkämper, D., Zurowietz, M., Beszteri, B., and Nattkemper, T. W. (2020). Deep learning-based diatom taxonomy on virtual slides. Scientific Reports 10:14416.
Kloster, M., et al., 2020. Deep learning-based diatom taxonomy on virtual slides. Scientific Reports, 10(1): 14416.
M. Kloster, et al., “Deep learning-based diatom taxonomy on virtual slides”, Scientific Reports, vol. 10, 2020, : 14416.
Kloster, M., Langenkämper, D., Zurowietz, M., Beszteri, B., Nattkemper, T.W.: Deep learning-based diatom taxonomy on virtual slides. Scientific Reports. 10, : 14416 (2020).
Kloster, Michael, Langenkämper, Daniel, Zurowietz, Martin, Beszteri, Bánk, and Nattkemper, Tim Wilhelm. “Deep learning-based diatom taxonomy on virtual slides”. Scientific Reports 10.1 (2020): 14416.
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