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Rapid and flexible segmentation of electron microscopy data using few-shot machine learning

Engineering and Technology

Rapid and flexible segmentation of electron microscopy data using few-shot machine learning

S. Akers, E. Kautz, et al.

Unlock new possibilities in materials science with a flexible, semi-supervised few-shot machine learning approach for automated segmentation of scanning transmission electron microscopy images. This innovative research, conducted by Sarah Akers, Elizabeth Kautz, Andrea Trevino-Gavito, Matthew Olszta, Bethany E. Matthews, Le Wang, Yingge Du, and Steven R. Spurgeon, enhances rapid image classification and microstructural feature mapping for advanced characterization techniques.

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