Publication detail

Towards Phytoplankton Parasite Detection Using Autoencoders

BILÍK, Š. BAKTRAKHANOV, D. EEROLA, T. HARAGUCHI, L. KRAFT, K. VAN DEN WYNGAERT, S. KANGAS, J. SJÖQVIST, C. MADSEN, K. LENSU, L. KÄLVIÄINEN, H. HORÁK, K.

Original Title

Towards Phytoplankton Parasite Detection Using Autoencoders

Type

journal article in Web of Science

Language

English

Original Abstract

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.

Keywords

Phytoplankton anomalies, Phytoplankton parasites, Anomaly detection, Autoencoders, Object detection, Faster R-CNN

Authors

BILÍK, Š.; BAKTRAKHANOV, D.; EEROLA, T.; HARAGUCHI, L.; KRAFT, K.; VAN DEN WYNGAERT, S.; KANGAS, J.; SJÖQVIST, C.; MADSEN, K.; LENSU, L.; KÄLVIÄINEN, H.; HORÁK, K.

Released

13. 9. 2023

Publisher

Springer

ISBN

1432-1769

Periodical

Machine Vision and Applications

Year of study

34

Number

6

State

United States of America

Pages from

1

Pages to

18

Pages count

18

URL

https://link.springer.com/article/10.1007/s00138-023-01450-x

Full text in the Digital Library

http://hdl.handle.net/11012/214455

BibTex

@article{BUT184624,
  author="Šimon {Bilík} and Daniel {Baktrakhanov} and Tuomas {Eerola} and Lumi {Haraguchi} and Kaisa {Kraft} and Silke {Van den Wyngaert} and Jonna {Kangas} and Conny {Sjöqvist} and Karin {Madsen} and Lasse {Lensu} and Heikki {Kälviäinen} and Karel {Horák}",
  title="Towards Phytoplankton Parasite Detection Using Autoencoders",
  journal="Machine Vision and Applications",
  year="2023",
  volume="34",
  number="6",
  pages="1--18",
  doi="10.1007/s00138-023-01450-x",
  issn="1432-1769",
  url="https://link.springer.com/article/10.1007/s00138-023-01450-x"
}