# Overcoming Labelled Data Scarcity for Defect Classification in Scanning Tunneling Microscopy

arXiv: https://arxiv.org/abs/2506.01678 · HTML: https://mlsanity.com/paper/2506.01678/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 57 references that are in the map.
- First posted on arXiv 2 Jun 2025.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
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- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning](https://mlsanity.com/paper/1911.04623/) (2019)
- [A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2205.06743/) (2022)
- [MetaAudio: A Few-Shot Audio Classification Benchmark](https://mlsanity.com/paper/2204.02121/) (2022)
- [Introducing Hann windows for reducing edge-effects in patch-based image segmentation](https://mlsanity.com/paper/1910.07831/) (2019)
- [Proto-CLIP: Vision-Language Prototypical Network for Few-Shot Learning](https://mlsanity.com/paper/2307.03073/) (2023)
- [Leveraging generative adversarial networks to create realistic scanning transmission electron microscopy images](https://mlsanity.com/paper/2301.07743/) (2023)
- [Learning on the Edge: Explicit Boundary Handling in CNNs](https://mlsanity.com/paper/1805.03106/) (2018)
- [Embedding Human Heuristics in Machine-Learning-Enabled Probe Microscopy](https://mlsanity.com/paper/1907.13401/) (2019)
- [TorchBench: Benchmarking PyTorch with High API Surface Coverage](https://mlsanity.com/paper/2304.14226/) (2023)
- [Counting Molecules: Python based scheme for automated enumeration and categorization of molecules in scanning tunneling microscopy images](https://mlsanity.com/paper/2203.01998/) (2022)

## Abstract (arXiv)

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 TiO2(110), including adsorbed AsH3 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.