ADP-FL adapts differential privacy mechanisms in federated learning to achieve higher segmentation accuracy, better boundaries, faster convergence, and stability across skin, kidney, and brain imaging tasks while preserving privacy.
A survey of generalization and adaptation in medical imaging foundation models
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.IV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
ADP-FL-MedSeg: Adaptive Differential Privacy for Federated Medical Segmentation Across Diverse Modalities
ADP-FL adapts differential privacy mechanisms in federated learning to achieve higher segmentation accuracy, better boundaries, faster convergence, and stability across skin, kidney, and brain imaging tasks while preserving privacy.