ArticleScientific reports2020
Annotation-free learning of plankton for classification and anomaly detection.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Computer Vision-Assisted Semiautomatic Analysis of Zooplankton in a Longitudinal Study of the Ecological State of Lake Baikal.Biology · 2026Article
- The Impact of Intelligent Medical Systems Combined with Interdisciplinary Care Teams in Home-Based Care Services for Disabled Elderly Individuals: A Prospective Intervention Study.Journal of multidisciplinary healthcare · 2026Article
- Scaling down annotation needs: The capacity of self-supervised learning on diatom classification.iScience · 2025Article
- Detecting abnormal cell behaviors from dry mass time series.Scientific reports · 2024Article
- Review
- In-domain versus out-of-domain transfer learning in plankton image classification.Scientific reports · 2023Article
- Towards Automated Classification of Zooplankton Using Combination of Laser Spectral Techniques and Advanced Chemometrics.Sensors (Basel, Switzerland) · 2022Article
- A semi-automatic toolbox for markerless effective semantic feature extraction.Scientific reports · 2022Article
- Plankton classification with high-throughput submersible holographic microscopy and transfer learning.BMC ecology and evolution · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The acquisition of increasingly large plankton digital image datasets requires automatic methods of recognition and classification. As data size and collection speed increases, manual annotation and database representation are often bottlenecks for utilization of machine learning algorithms for taxonomic classification of plankton species in field studies. In this paper we present a novel set of algorithms to perform accurate detection and classification of plankton species with minimal supervision. Our algorithms approach the performance of existing supervised machine learning algorithms when tested on a plankton dataset generated from a custom-built lensless digital device. Similar results are obtained on a larger image dataset obtained from the Woods Hole Oceanographic Institution. Additionally, we introduce a new algorithm to perform anomaly detection on unclassified samples. Here an anomaly is defined as a significant deviation from the established classification. Our algorithms are designed to provide a new way to monitor the environment with a class of rapid online intelligent detectors.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.