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Distributed Contrastive Learning for Medical Image Segmentation
[article]
2022
arXiv
pre-print
Supervised deep learning needs a large amount of labeled data to achieve high performance. However, in medical imaging analysis, each site may only have a limited amount of data and labels, which makes learning ineffective. Federated learning (FL) can learn a shared model from decentralized data. But traditional FL requires fully-labeled data for training, which is very expensive to obtain. Self-supervised contrastive learning (CL) can learn from unlabeled data for pre-training, followed by
arXiv:2208.03808v1
fatcat:3ruylvqxkfbzbo7db5oiafsupa