Federated contrastive learning for decentralized unlabeled medical images

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...

Полное описание

Библиографические подробности
Главные авторы:	Dong, N, Voiculescu, ID
Формат:	Conference item
Язык:	English
Опубликовано:	Springer 2021

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