Rapid and flexible segmentation of electron microscopy data using few-shot machine learning
Abstract Automatic segmentation of key microstructural features in atomic-scale electron microscope images is critical to improved understanding of structure–property relationships in many important materials and chemical systems. However, the present paradigm involves time-intensive manual analysis...
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Autores principales: | Sarah Akers, Elizabeth Kautz, Andrea Trevino-Gavito, Matthew Olszta, Bethany E. Matthews, Le Wang, Yingge Du, Steven R. Spurgeon |
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Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
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Materias: | |
Acceso en línea: | https://doaj.org/article/33d1bedf318a45748896921c9d1f1190 |
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