Deep learning-based diatom taxonomy on virtual slides
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 c...
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ftpubmed:oai:pubmedcentral.nih.gov:7468105 2023-05-15T18:25:32+02:00 Deep learning-based diatom taxonomy on virtual slides Kloster, Michael Langenkämper, Daniel Zurowietz, Martin Beszteri, Bánk Nattkemper, Tim W. 2020-09-02 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7468105/ http://www.ncbi.nlm.nih.gov/pubmed/32879374 https://doi.org/10.1038/s41598-020-71165-w en eng Nature Publishing Group UK http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7468105/ http://www.ncbi.nlm.nih.gov/pubmed/32879374 http://dx.doi.org/10.1038/s41598-020-71165-w © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. CC-BY Sci Rep Article Text 2020 ftpubmed https://doi.org/10.1038/s41598-020-71165-w 2020-09-06T01:02:37Z 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. Text Southern Ocean PubMed Central (PMC) Southern Ocean Scientific Reports 10 1 |
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Article Kloster, Michael Langenkämper, Daniel Zurowietz, Martin Beszteri, Bánk Nattkemper, Tim W. Deep learning-based diatom taxonomy on virtual slides |
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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. |
format |
Text |
author |
Kloster, Michael Langenkämper, Daniel Zurowietz, Martin Beszteri, Bánk Nattkemper, Tim W. |
author_facet |
Kloster, Michael Langenkämper, Daniel Zurowietz, Martin Beszteri, Bánk Nattkemper, Tim W. |
author_sort |
Kloster, Michael |
title |
Deep learning-based diatom taxonomy on virtual slides |
title_short |
Deep learning-based diatom taxonomy on virtual slides |
title_full |
Deep learning-based diatom taxonomy on virtual slides |
title_fullStr |
Deep learning-based diatom taxonomy on virtual slides |
title_full_unstemmed |
Deep learning-based diatom taxonomy on virtual slides |
title_sort |
deep learning-based diatom taxonomy on virtual slides |
publisher |
Nature Publishing Group UK |
publishDate |
2020 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7468105/ http://www.ncbi.nlm.nih.gov/pubmed/32879374 https://doi.org/10.1038/s41598-020-71165-w |
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Southern Ocean |
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Southern Ocean |
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Southern Ocean |
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Southern Ocean |
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Sci Rep |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7468105/ http://www.ncbi.nlm.nih.gov/pubmed/32879374 http://dx.doi.org/10.1038/s41598-020-71165-w |
op_rights |
© The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
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CC-BY |
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https://doi.org/10.1038/s41598-020-71165-w |
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Scientific Reports |
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