Applied Plankton Image Classification for Imaging FlowCytobot Data
Name
duckworth-birdy-meng-eecs-2025-thesis.pdf
Description
Thesis PDF
Size
8.96 MB
Format
Adobe PDF
Checksum (MD5)
e42405b9a6f627aca483676f1fd6ca66
Author(s)
Duckworth, Barbara R.
Advisor(s)
Follows, Michael J.
Date Issued
February 2025
Publisher
Massachusetts Institute of Technology
Abstract
As the ability to gather vast quantities of data from oceanographic bioimaging sensors increases, so too does the need to process, analyze, and store that data in a consistent, standard way that enables replicability and accessibility for future studies. The Imaging FlowCytobot (IFCB), an automated submersible flow cytometer, produces high resolution images of plankton at rates up to 10 Hz for months or years, resulting in billions of images. This project compares various methods to categorize incoming images of plankton gathered by the IFCB - Convolutional Neural Nets (CNNs), Vision Transformers (ViT), and self-supervised learning (MAE). The benefits and downsides of each model are analyzed and discussed for future IFCB operators to process their data using the methods that best align with their research questions, along with step-by-step explanations about the pros and cons of each method depending on the use case.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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In Copyright - Educational Use Permitted
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