Design of a Graphical User Interface for Few-Shot Machine Learning Classification of Electron Microscopy Data
This work addresses usability issues for researchers in electron microscopy, but it is incremental as it focuses on interface design rather than algorithmic innovation.
The authors tackled the problem of slow and unintuitive command-line tools for few-shot machine learning classification of electron microscopy data by developing a Python-based graphical user interface, which enables easy visualization and real-time feedback for users.
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.