Federated contrastive learning for decentralized unlabeled medical images
A label-efficient paradigm in computer vision is based on self-supervised contrastive pre-training on unlabeled data followed by fine-tuning with a small number of labels. Making practical use of a federated computing environment in the clinical domain and learning on medical images poses specific c...
| Auteurs principaux: | , |
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| Format: | Conference item |
| Langue: | English |
| Publié: |
Springer
2021
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