A Data-Efficient Deep Learning Workflow for Atomic-Level Segmentation of In Situ High-Resolution Transmission Electron Microscopy Images With Heterogeneous Backgrounds.
- DOI
- 10.1093/mam/ozag082
- Published
- 2026-07-01
- Container
- Microsc Microanal
- Publisher
- Not recorded
- Open access
- no
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Cite this work
BibTeX
@article{allodium:10.1093/mam/ozag082,
title = {A Data-Efficient Deep Learning Workflow for Atomic-Level Segmentation of In Situ High-Resolution Transmission Electron Microscopy Images With Heterogeneous Backgrounds.},
author = {Lee B and Li M and Huffman M and McEver JG and Chen X and Saidi WA and Yang J.},
year = {2026},
journal = {Microsc Microanal},
doi = {10.1093/mam/ozag082},
url = {https://doi.org/10.1093/mam/ozag082}
}RIS
TY - JOUR TI - A Data-Efficient Deep Learning Workflow for Atomic-Level Segmentation of In Situ High-Resolution Transmission Electron Microscopy Images With Heterogeneous Backgrounds. AU - Lee B AU - Li M AU - Huffman M AU - McEver JG AU - Chen X AU - Saidi WA AU - Yang J. PY - 2026 JO - Microsc Microanal DO - 10.1093/mam/ozag082 UR - https://doi.org/10.1093/mam/ozag082 ER -
APA
B, L., M, L., M, H., JG, M., X, C., WA, S., & J., Y. (2026). A Data-Efficient Deep Learning Workflow for Atomic-Level Segmentation of In Situ High-Resolution Transmission Electron Microscopy Images With Heterogeneous Backgrounds.. Microsc Microanal. https://doi.org/10.1093/mam/ozag082
Source records
- europe-pmc · retrieved 2026-09-24T21:04:03.848Z