# Semi-supervised machine learning model for analysis of nanowire morphologies from transmission electron microscopy images

arXiv: https://arxiv.org/abs/2203.13875 · HTML: https://mlsanity.com/paper/2203.13875/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 63 references that are in the map.
- First posted on arXiv 25 Mar 2022.

## Builds on (most cited first, 15 of 15)

- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [Self-supervised Learning: Generative or Contrastive](https://mlsanity.com/paper/2006.08218/) (2020)
- [Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1708.08987/) (2017)
- [Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks](https://mlsanity.com/paper/2103.14749/) (2021)
- [Self supervised contrastive learning for digital histopathology](https://mlsanity.com/paper/2011.13971/) (2020)
- [Variational Autoencoder for Deep Learning of Images, Labels and Captions](https://mlsanity.com/paper/1609.08976/) (2016)
- [Visualizing Data using GTSNE](https://mlsanity.com/paper/2108.01301/) (2021)
- [A Transfer Learning Approach for Microstructure Reconstruction and Structure-property Predictions](https://mlsanity.com/paper/1805.02784/) (2018)
- [Review: Deep Learning in Electron Microscopy](https://mlsanity.com/paper/2009.08328/) (2020)
- [Advanced Steel Microstructural Classification by Deep Learning Methods](https://mlsanity.com/paper/1706.06480/) (2017)
- [Machine Learning Pipeline for Segmentation and Defect Identification from High Resolution Transmission Electron Microscopy Data](https://mlsanity.com/paper/2001.05022/) (2020)
- [Single-shot autofocusing of microscopy images using deep learning](https://mlsanity.com/paper/2003.09585/) (2020)
- [On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathy](https://mlsanity.com/paper/2106.13497/) (2021)
- [Toward quantitative fractography using convolutional neural networks](https://mlsanity.com/paper/1908.02242/) (2019)
- [The Adoption of Image-Driven Machine Learning for Microstructure Characterization and Materials Design: A Perspective](https://mlsanity.com/paper/2105.09729/) (2021)

## Built on by (most cited first, 2 of 2)

- [Combining Variational Autoencoders and Physical Bias for Improved Microscopy Data Analysis](https://mlsanity.com/paper/2302.04216/) (2023)
- [AI-assisted Human-in-the-Loop Web Platform for Structural Characterization in Hard drive design](https://mlsanity.com/paper/2604.00359/) (2026)

## Abstract (arXiv)

In the field of materials science, microscopy is the first and often only accessible method for structural characterization. There is a growing interest in the development of machine learning methods that can automate the analysis and interpretation of microscopy images. Typically training of machine learning models requires large numbers of images with associated structural labels, however, manual labeling of images requires domain knowledge and is prone to human error and subjectivity. To overcome these limitations, we present a semi-supervised transfer learning approach that uses a small number of labeled microscopy images for training and performs as effectively as methods trained on significantly larger image datasets. Specifically, we train an image encoder with unlabeled images using self-supervised learning methods and use that encoder for transfer learning of different downstream image tasks (classification and segmentation) with a minimal number of labeled images for training. We test the transfer learning ability of two self-supervised learning methods: SimCLR and Barlow-Twins on transmission electron microscopy (TEM) images. We demonstrate in detail how this machine learning workflow applied to TEM images of protein nanowires enables automated classification of nanowire morphologies (e.g., single nanowires, nanowire bundles, phase separated) as well as segmentation tasks that can serve as groundwork for quantification of nanowire domain sizes and shape analysis. We also extend the application of the machine learning workflow to classification of nanoparticle morphologies and identification of different type of viruses from TEM images.