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Federated Learning for Breast Density Classification: A Real-World Implementation
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“The real-world implementation of FL for medical imaging is also presented in [108] for breast density classification, showing that the FL scheme can perform 6.3% on average better than standalone training approaches.”
From Federated learning for smart healthcare: A survey (2023) · cites this paper as [108]
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Dayan, I., Roth, H. R., Zhong, A., Harouni, A., Gentili, A., Abidin, A. Z., Liu, A., Costa, A. B., Wood, B. J., Tsai, C.-S., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01506-3 · Cited by 842
Self-citationFederated learning for smart healthcare: A survey
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Metadata
- Authors
- Roth, H. R., Chang, K., Singh, P., Neumark, N., Li, W., Gupta, V., Gupta, S., Qu, L., Ihsani, A., Bizzo, B. C., Wen, Y., Buch, V., Shah, M., Kitamura, F., Mendonça, M., Lavor, V., Harouni, A., Compas, C., Tetreault, J., Dogra, P., Cheng, Y., Erdal, S., White, R., Hashemian, B., Schultz, T., Zhang, M., McCarthy, A., Yun, B. M., Sharaf, E., Hoebel, K. V., Patel, J. B., Chen, B., Ko, S., Leibovitz, E., Pisano, E. D., Coombs, L., Xu, D., Dreyer, K. J., Dayan, I., Naidu, R. C., Flores, M., Rubin, D., & Kalpathy-Cramer, J.
- Affiliations
- Nvidia (United States) (US)
- Massachusetts General Hospital (US)
- Mayo Clinic in Florida (US)
- Stanford University (US)
- American College of Radiology (US)
- DASA (Brazil) (BR)
- Massachusetts Institute of Technology (US)
- Corresponding author
- Roth, Holger R.
- ORCID
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 181-191
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3083403771
- Cited by
- 202
- References
- 22
- Citations by year
- 2026: 20 · 2025: 34 · 2024: 31 · 2023: 58 · 2022: 38 · 2021: 21
- FWCI
- 11.17
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- The HKU Scholars Hub (University of Hong Kong) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Digital Radiography and Breast Imaging
- Keywords
- federated learning, breast density classification, BI-RADS, mammography, cross-institutional collaboration, model generalization, data privacy, deep learning
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