This review highlights federated learning's effectiveness in medical imaging, addressing privacy and data sharing challenges during AI model development.
Key Points
Federated learning enhances the joint training of robust models on diverse datasets while ensuring patient privacy.
It supports local fine-tuning of models for tumor diagnosis, enabling continuous updates without risking data centralization.
The review covers the contributions of federated learning across the medical imaging pipeline, from CT/MRI reconstruction to diagnostic applications.
Future directions in federated learning research could significantly advance medical applications and improve model performance.