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Self-Ensembling Contrastive Learning for Semi-Supervised Medical Image Segmentation
[article]
2021
arXiv
pre-print
Deep learning has demonstrated significant improvements in medical image segmentation using a sufficiently large amount of training data with manual labels. Acquiring well-representative labels requires expert knowledge and exhaustive labors. In this paper, we aim to boost the performance of semi-supervised learning for medical image segmentation with limited labels using a self-ensembling contrastive learning technique. To this end, we propose to train an encoder-decoder network at image-level
arXiv:2105.12924v2
fatcat:sadhwoifbbbn7otbe4j6urtqji