Combining high-throughput imaging flow cytometry and deep learning for efficient species and life-cycle stage identification of phytoplankton

Automated plant species identification—Trends and future directions

(2018) Jana Wäldchen et al.   PLoS Computational Biology

Imaging flow cytometry for phytoplankton analysis

(2017) Veronika Dashkova et al.   METHODS

Acquiring and preprocessing leaf images for automated plant identification: understanding the tradeoff between effort and information gain

(2017) Michael Rzanny et al.   Plant Methods

Automated Diatom Classification (Part B): A Deep Learning Approach

(2017) Anibal Pedraza et al.   Applied Sciences-Basel

Deep Learning in Label-free Cell Classification

(2016) Claire Lifan Chen et al.   Scientific Reports

Taxonomic Classification of Phytoplankton with Multivariate Optical Computing, Part II: Design and Experimental Protocol of a Shipboard Fluorescence Imaging Photometer

(2013) Joseph A. Swanstrom et al.   APPLIED SPECTROSCOPY

PlanktoVision – an automated analysis system for the identification of phytoplankton

(2013) Katja Schulze et al.   BMC BIOINFORMATICS

A phytoplankton trophic index to assess the status of lakes for the Water Framework Directive

(2012) Geoff Phillips et al.   HYDROBIOLOGIA

Performance of the human "counting machine": evaluation of manual microscopy for enumerating plankton

(2012) M. R. First et al.   JOURNAL OF PLANKTON RESEARCH

Functional analysis and classification of phytoplankton based on data from an automated flow cytometer

(2011) Anthony Malkassian et al.   CYTOMETRY PART A

An Automated Platform for Phytoplankton Ecology and Aquatic Ecosystem Monitoring

(2011) Francesco Pomati et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY