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Decouple, Purify and Unite: Semantic-Structural Prototype Learning for Federated Medical Segmentation
Federated learning enables medical institutions to train a global model without sharing data, yet feature heterogeneity from diverse scanners or protocols remains challenging. Existing representation-based methods face two limitations: 1) Incomplete Contextual Representation Learning: single-layer o
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