ArticleComputational and structural biotechnology journal2025
Towards generalizable Federated Learning in medical imaging: A real-world case study on mammography data.
Article in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Artificial intelligence in paediatric chest imaging: applications, challenges, and future directions.Pediatric radiology · 2026Review
- Comparative Performance Evaluation of Federated and Centralized Learning for Velum and OTE Segmentation in Sleep Endoscopy Images.Journal of imaging informatics in medicine · 2026Article
- GEN-Guard: correcting generalization failures for deployable federated surgical AI.International journal of computer assisted radiology and surgery · 2026Article
- Federated Learning in Healthcare: From Research to Real-World Deployment.Annual review of biomedical engineering · 2026Review
- Review
- Artificial intelligence in orthopedic regenerative medicine: from design to clinical translational pathways.Frontiers in cell and developmental biology · 2026Review
- Training Together, Diagnosing Better: Federated Learning for Collagen VI-Related Dystrophies.ArXiv · 2025Article
- Federated multimodal AI for precision-equitable diabetes care.Frontiers in digital health · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Federated Learning has been rapidly gaining in popularity in medical applications, due to the increased privacy offered, since medical data doesn't need to leave the hospitals' premises for AI model training. However, a direct translation of a classic experiment to a federated one is not always straightforward. In this work, we delve into the intricacies of federated learning for a breast cancer classification tool. We compare classic model training with a federated variant, and highlight the adaptations that need to be taken care of to ensure the equivalence between the two. Specifically, we introduce the Breast Area Detection tool as an essential component of the pre-processing pipeline to enhance the robustness of Federated Learning by offering data harmonization. On top of that, we present an end-to-end Federated Learning framework that is effective for real-world data and scenarios. Among the three real-world hospitals involved in the experimental procedure, the proposed framework significantly improves performance at the first hospital, providing consistent results similar to those achieved in the classic approach. Experimental results demonstrate that the interventions introduced improved model performance by approximately 35%, aligning federated learning and centralized model performance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.