Fig. 1: Few-shot model architecture.
From: Rapid and flexible segmentation of electron microscopy data using few-shot machine learning
The raw STO/Ge image (a) is broken into several smaller chips (b) and a few user defined chips are used to represent desired segmentation classes in the support set (c). Each chip then acts as a query and is compared against a prototype (d), defined by the support set, and categorized according to the minimum Euclidean distance between the query and each prototype, yielding the segmented image (e). Scale bar = 5 nm.
