Overcoming Data Scarcity in Scanning Tunnelling Microscopy Image Segmentation

TL;DR

This paper introduces a novel automated segmentation method for STM images that combines few-shot and unsupervised learning, reducing the need for large annotated datasets and enabling quick adaptation to new surfaces.

Contribution

It presents a flexible, semi-supervised approach that generalizes well across different surfaces and requires minimal additional data for new surface adaptation.

Findings

Effective recognition of atomic features on multiple surfaces

Strong generalization capabilities of the model

Ability to adapt with as few as one new labeled data point

Abstract

Scanning tunnelling microscopy (STM) is a powerful technique for imaging surfaces with atomic resolution, providing insight into physical and chemical processes at the level of single atoms and molecules. A regular task of STM image analysis is the identification and labelling of features of interest against a uniform background. Performing this manually is a labour-intensive task, requiring significant human effort. To reduce this burden, we propose an automated approach to the segmentation of STM images that uses both few-shot learning and unsupervised learning. Our technique offers greater flexibility compared to previous supervised methods; it removes the requirement for large manually annotated datasets and is thus easier to adapt to an unseen surface while still maintaining a high accuracy. We demonstrate the effectiveness of our approach by using it to recognise atomic features on three distinct surfaces: Si(001), Ge(001), and TiO$_2$(110), including adsorbed AsH$_3$ molecules on the silicon and germanium surfaces. Our model exhibits strong generalisation capabilities, and following initial training, can be adapted to unseen surfaces with as few as one additional labelled data point. This work is a significant step towards efficient and material-agnostic, automatic segmentation of STM images.