FAME: A privacy-preserving dual-stage deep learning framework for breast ultrasound imaging using federated transfer and synthetic learning
Federated Attention-guided Multi-task Ensemble offers a scalable, privacy-compliant, and high-performing solution for multi-institutional breast ultrasound analysis. By combining federated learning, synthetic augmentation, and attention mechanisms, it provides a strong foundation for secure, collaborative breast cancer diagnosis.
Authors
- Malik Abdul Manan (ORCID: https://orcid.org/0009-0007-8644-4655)
- Shahzad Ahmed (ORCID: https://orcid.org/0000-0002-6341-9904)
- Fahad Sabah (ORCID: https://orcid.org/0000-0003-1558-2616)
- Abdul Raheem (ORCID: https://orcid.org/0000-0002-1140-3230)
- Sadique Ahmad (ORCID: https://orcid.org/0000-0001-6907-2318)
- Ala Saleh Alluhaidan (ORCID: https://orcid.org/0000-0001-6829-9705)
- Zhen Yang (ORCID: https://orcid.org/0000-0002-6058-0217)
Institutions
- Princess Nourah bint Abdulrahman University (SA)
- Prince Sultan University (SA)
- Beijing University of Technology (CN)
- Beijing University of Chemical Technology (CN)
Publication Details
- Journal
- Digital Health
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1177/20552076251390564
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00