Towards Phytoplankton Parasite Detection Using Autoencoders

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dc.contributor.authorBilík, Šimoncs
dc.contributor.authorBaktrakhanov, Danielcs
dc.contributor.authorEerola, Tuomascs
dc.contributor.authorHaraguchi, Lumics
dc.contributor.authorKraft, Kaisacs
dc.contributor.authorVan den Wyngaert, Silkecs
dc.contributor.authorKangas, Jonnacs
dc.contributor.authorSjöqvist, Connycs
dc.contributor.authorMadsen, Karincs
dc.contributor.authorLensu, Lassecs
dc.contributor.authorKälviäinen, Heikkics
dc.contributor.authorHorák, Karelcs
dc.coverage.issue6cs
dc.coverage.volume34cs
dc.date.issued2023-09-13cs
dc.description.abstractPhytoplankton 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.en
dc.formattextcs
dc.format.extent1-18cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationMachine Vision and Applications. 2023, vol. 34, issue 6, p. 1-18.en
dc.identifier.doi10.1007/s00138-023-01450-xcs
dc.identifier.issn1432-1769cs
dc.identifier.orcid0000-0001-8797-7700cs
dc.identifier.orcid0000-0002-2280-3029cs
dc.identifier.other184624cs
dc.identifier.researcheridJEP-7714-2023cs
dc.identifier.scopus57222421244cs
dc.identifier.urihttp://hdl.handle.net/11012/214455
dc.language.isoencs
dc.publisherSpringercs
dc.relation.ispartofMachine Vision and Applicationscs
dc.relation.urihttps://link.springer.com/article/10.1007/s00138-023-01450-xcs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1432-1769/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectPhytoplankton anomaliesen
dc.subjectPhytoplankton parasitesen
dc.subjectAnomaly detectionen
dc.subjectAutoencodersen
dc.subjectObject detectionen
dc.subjectFaster R-CNNen
dc.titleTowards Phytoplankton Parasite Detection Using Autoencodersen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-184624en
sync.item.dbtypeVAVen
sync.item.insts2025.02.03 15:39:33en
sync.item.modts2025.01.17 15:30:32en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav automatizace a měřicí technikycs
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