Addressing data heterogeneity in distributed medical imaging with heterosync learning
Abstract Data heterogeneity critically limits distributed artificial intelligence (AI) in medical imaging. We propose HeteroSync Learning (HSL), a privacy-preserving framework that addresses heterogeneity through: (1) Shared Anchor Task (SAT) for cross-node representation alignment, and (2) an Auxil...
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| Principais autores: | , , , , , , , , , , , , , , , , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado: |
Nature Portfolio
2025-10-01
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| Series: | Nature Communications |
| Acceso en liña: | https://doi.org/10.1038/s41467-025-64459-y |
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