Development of Privacy-preserving Deep Learning Model with Homomorphic Encryption: A Technical Feasibility Study in Kidney CT Imaging
- DOI
- 10.1148/ryai.240798
- Published
- 2025-11-01
- Container
- Radiology: Artificial Intelligence
- Publisher
- Radiological Society of North America (RSNA)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1148/ryai.240798,
title = {Development of Privacy-preserving Deep Learning Model with Homomorphic Encryption: A Technical Feasibility Study in Kidney CT Imaging},
author = {Sang-Wook Lee and Jongmin Choi and Min-Je Park and Hajin Kim and Soo-Heang Eo and Garam Lee and Sulgi Kim and Jungyo Suh},
year = {2025},
journal = {Radiology: Artificial Intelligence},
doi = {10.1148/ryai.240798},
url = {https://doi.org/10.1148/ryai.240798}
}RIS
TY - JOUR TI - Development of Privacy-preserving Deep Learning Model with Homomorphic Encryption: A Technical Feasibility Study in Kidney CT Imaging AU - Sang-Wook Lee AU - Jongmin Choi AU - Min-Je Park AU - Hajin Kim AU - Soo-Heang Eo AU - Garam Lee AU - Sulgi Kim AU - Jungyo Suh PY - 2025 JO - Radiology: Artificial Intelligence DO - 10.1148/ryai.240798 UR - https://doi.org/10.1148/ryai.240798 ER -
APA
Lee, S., Choi, J., Park, M., Kim, H., Eo, S., Lee, G., Kim, S., & Suh, J. (2025). Development of Privacy-preserving Deep Learning Model with Homomorphic Encryption: A Technical Feasibility Study in Kidney CT Imaging. Radiology: Artificial Intelligence. https://doi.org/10.1148/ryai.240798
Source records
- crossref · retrieved 2026-09-26T02:07:40.236Z