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

arXiv: https://arxiv.org/abs/2107.10387 · HTML: https://mlsanity.com/paper/2107.10387/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 53 references that are in the map.
- First posted on arXiv 21 Jul 2021.

## Builds on (most cited first, 4 of 4)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Few-Shot Learning with Metric-Agnostic Conditional Embeddings](https://mlsanity.com/paper/1802.04376/) (2018)
- [Review: Deep Learning in Electron Microscopy](https://mlsanity.com/paper/2009.08328/) (2020)
- [High throughput quantitative metallography for complex microstructures using deep learning: A case study in ultrahigh carbon steel](https://mlsanity.com/paper/1805.08693/) (2018)

## Built on by (most cited first, 3 of 3)

- [An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analytics](https://mlsanity.com/paper/2109.14772/) (2021)
- [EasyMLServe: Easy Deployment of REST Machine Learning Services](https://mlsanity.com/paper/2211.14417/) (2022)
- [Deep-learning-based prediction of nanoparticle phase transitions during in situ transmission electron microscopy](https://mlsanity.com/paper/2205.11407/) (2022)

## Abstract (arXiv)

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.