CLUE: Contrastive Learning with Uncertainty Estimation for Semi-Supervised Medical Image Segmentation

초록

Deep learning based medical image segmentation requires high quality pixel-level labeled data, which demands significant time and cost. Most existing semi-supervised learning methods exclude pseudo-labels with high uncertainty from training. This causes class imbalance and limits deep representation learning. In this study, we propose a semi-supervised medical image segmentation method based on contrastive learning that leverages uncertainty information. The proposed method achieved DSC and Jaccard scores of 89.48 and 81.50, with only 10% labeled data, surpassing existing methods. © 2024 IEEE.

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