Extracting, Visualizing, and Learning from Dynamic Data: Perfusion in Surgical Video for Tissue Characterization
- DOI
- 10.1109/icdh55609.2022.00009
- Published
- 2022-07
- Container
- 2022 IEEE International Conference on Digital Health (ICDH)
- Publisher
- IEEE
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/icdh55609.2022.00009,
title = {Extracting, Visualizing, and Learning from Dynamic Data: Perfusion in Surgical Video for Tissue Characterization},
author = {Jonathan P. Epperlein and Niall P. Hardy and Pol Mac Aonghusa and Ronan A. Cahill},
year = {2022},
journal = {2022 IEEE International Conference on Digital Health (ICDH)},
doi = {10.1109/icdh55609.2022.00009},
url = {https://doi.org/10.1109/icdh55609.2022.00009}
}RIS
TY - JOUR TI - Extracting, Visualizing, and Learning from Dynamic Data: Perfusion in Surgical Video for Tissue Characterization AU - Jonathan P. Epperlein AU - Niall P. Hardy AU - Pol Mac Aonghusa AU - Ronan A. Cahill PY - 2022 JO - 2022 IEEE International Conference on Digital Health (ICDH) DO - 10.1109/icdh55609.2022.00009 UR - https://doi.org/10.1109/icdh55609.2022.00009 ER -
APA
Epperlein, J. P., Hardy, N. P., Aonghusa, P. M., & Cahill, R. A. (2022). Extracting, Visualizing, and Learning from Dynamic Data: Perfusion in Surgical Video for Tissue Characterization. 2022 IEEE International Conference on Digital Health (ICDH). https://doi.org/10.1109/icdh55609.2022.00009
Source records
- crossref · retrieved 2026-09-25T23:08:13.480Z