Towards Phytoplankton Parasite Detection Using Autoencoders
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU148989" target="_blank" >RIV/00216305:26220/23:PU148989 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/article/10.1007/s00138-023-01450-x" target="_blank" >https://link.springer.com/article/10.1007/s00138-023-01450-x</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s00138-023-01450-x" target="_blank" >10.1007/s00138-023-01450-x</a>
Alternative languages
Result language
angličtina
Original language name
Towards Phytoplankton Parasite Detection Using Autoencoders
Original language description
Phytoplankton parasites are largely understudied microbial components with a potentially significant ecological influence on phytoplankton bloom dynamics. To better understand the impact of phytoplankton parasites, improved detection methods are needed to integrate phytoplankton parasite interactions into monitoring of aquatic ecosystems. Automated imaging devices commonly produce vast amounts of phytoplankton image data, but the occurrence of anomalous phytoplankton data in such datasets is rare. Thus, we propose an unsupervised anomaly detection system based on the similarity between the original and autoencoder-reconstructed samples. With this approach, we were able to reach an overall F1 score of 0.75 in nine phytoplankton species, which could be further improved by species-specific fine-tuning. The proposed unsupervised approach was further compared with the supervised Faster R-CNN-based object detector. Using this supervised approach and the model trained on plankton species and anomalies, we were able to reach a highest F1 score of 0.86. However, the unsupervised approach is expected to be more universal as it can also detect unknown anomalies and it does not require any annotated anomalous data that may not always be available in sufficient quantities. Although other studies have dealt with plankton anomaly detection in terms of non-plankton particles or air bubble detection, our paper is, according to our best knowledge, the first that focuses on automated anomaly detection considering putative phytoplankton parasites or infections.
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Machine Vision and Applications
ISSN
0932-8092
e-ISSN
1432-1769
Volume of the periodical
34
Issue of the periodical within the volume
6
Country of publishing house
US - UNITED STATES
Number of pages
18
Pages from-to
1-18
UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-85171201306