Dual Contrastive Learning with Mutual Correction for Semi-Supervised Medical Image Segmentation

Semi-supervised learning has garnered considerable attention from researchers due to its capacity to utilize extensive amounts of unlabeled data, thus reducing the reliance of deep learning models on annotated datasets. However, in medical image segmentation, this method still encounters challenges such as suboptimal pseudo-labeling and insufficient feature extraction because of the confirmation bias problem induced by erroneously fitting unlabeled data. To tackle these challenges, we propose a novel approach that integrates correction modules and contrastive learning. First, our method exploits the difference in the output predictions from two different decoders and employs two rectification losses in the inconsistent regions for labeled and unlabeled data respectively, which mitigates the confirmation bias problem. Additionally, we incorporate two uncertainty-guided pixel-prototype contrastive learning modules, which are designed to perceive complete sample distribution information and optimize the features of pixels with low-uncertainty pseudo labels. Both modules complement each other and enable the encoder to generate class-discriminative features, thereby enhancing the final segmentation performance. Finally, extensive experiments are conducted on the two widely used medical image datasets to demonstrate the effectiveness of our method.