Few-Shot Machine Learning Classification of Electron Microscopy Data

This is a UW-DIRECT capstone project cooperated with Pacific Northwest National Laboratory (PNNL) and mentored by Marjolein Oostrom, Sarah Akers and Dr. Steven R. Spurgeon. We proposed a few-Shot machine learning algorithum for classification of electron microscopy images and implemented a graphical-user interface for it. I am responsible for implementing training loop for few-shot machine learning analysis of electron microscope data. Transfer learning is utilized to improvement performance of resent on feature extraction of microscope images. The abstract is as follows:

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.