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Learning to segment with limited annotations: Self-supervised pretraining with regression and contrastive loss in MRI
In this work, we consider two pre-training approaches for driving a DL model to learn different representations using: a) regression loss that exploits spatial dependencies within an image and b) contrastive loss that exploits semantic similarity between pairs of images.
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