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...
| 主要な著者: | , |
|---|---|
| フォーマット: | Conference item |
| 言語: | English |
| 出版事項: |
Springer
2021
|