Design of a Graphical User Interface for Few-Shot Machine Learning Classification of Electron Microscopy Data

• URL: http://arxiv.org/abs/2107.10387v1
• Date: Wed, 21 Jul 2021 23:02:33 GMT
• Title: Design of a Graphical User Interface for Few-Shot Machine Learning Classification of Electron Microscopy Data
• Authors: Christina Doty, Shaun Gallagher, Wenqi Cui, Wenya Chen, Shweta Bhushan, Marjolein Oostrom, Sarah Akers, Steven R. Spurgeon
• Abstract summary: We develop a Python-based graphical user interface that enables end users to easily conduct and visualize the output of few-shot learning models. This interface is lightweight and can be hosted locally or on the web, providing the opportunity to reproducibly conduct, share, and crowd-source few-shot analyses.
• Score: 0.23453441553817042
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The recent growth in data volumes produced by modern electron microscopes requires rapid, scalable, and flexible approaches to image segmentation and analysis. Few-shot machine learning, which can richly classify images from a handful of user-provided examples, is a promising route to high-throughput analysis. However, current command-line implementations of such approaches can be slow and unintuitive to use, lacking the real-time feedback necessary to perform effective classification. Here we report on the development of a Python-based graphical user interface that enables end users to easily conduct and visualize the output of few-shot learning models. This interface is lightweight and can be hosted locally or on the web, providing the opportunity to reproducibly conduct, share, and crowd-source few-shot analyses.

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