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H2ASeg: Hierarchical Adaptive Interaction and Weighting Network for Tumor Segmentation in PET/CT Images
However, modality-specific encoders used in these methods operate independently, inadequately leveraging the synergistic relationships inherent in PET and CT modalities, for example, the complementarity between semantics and structure.
Decentralized Gossip Mutual Learning (GML) for brain tumor segmentation on multi-parametric MRI
Federated Learning (FL) enables collaborative model training among medical centers without sharing private data.
Decentralized Gossip Mutual Learning (GML) for automatic head and neck tumor segmentation
Federated learning (FL) has emerged as a promising strategy for collaboratively training complicated machine learning models from different medical centers without the need of data sharing.
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