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Semi-supervised learning of imaging data
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April 23, 2024

Ziatdinov et al highlight_3_edited.jpg

Schematic of the rotationally-invariant semi-supervised variational autoencoder (ss-rVAE, left) and disentanglement of representations for SEM images of gold nanoparticles create a nanoparticle library (right).

Scientific Achievement

We demonstrate a powerful approach for the classification of imaging data by generalizing learning from a small, labeled subset to a larger, unlabeled dataset where features of interest have strong rotational and positional disorder.​

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Significance and Impact

The analysis of large datasets where clusters of nanoparticles exhibit positional and rotational disorder is a foundational problem in imaging methods ranging from optical to electron and scanning probe microscopy.​

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Research Details

  • Developed a semi-supervised ss-rVAE to classify imaging data with significant noise.

  • Demonstrated improved accuracy and robustness on synthetic datasets.

  • Applied the ss-rVAE to experimental SEM data, effectively classifying gold nanoparticles at arbitrary rotations. 

Ziatdinov, M.A., M.Y. Yaman, Y. Liu, D. Ginger, and S.V. Kalinin. (2024). Semi-supervised learning of images with strong rotational disorder: Assembling nanoparticle libraries. Digital Discovery, 3, 1213-1220. DOI: 10.1039/D3DD00196B 
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Work performed at the University of Washington and Pacific Northwest National Laboratory

Thrust 1: Emergence of Order: Research

HIGHLIGHT

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