Dynamic graph consistency and self-contrast learning for semi-supervised medical image segmentation

Semi-supervised medical image segmentation endeavors to exploit a limited set of labeled data in conjunction with a substantial corpus of unlabeled data, with the aim of training models that can match or even exceed the efficacy of fully supervised segmentation models. Despite the potential of this approach, most existing semi-supervised medical image segmentation techniques that employ consistency regularization predominantly focus on spatial consistency at the image level, often neglecting the crucial role of feature-level channel information. To address this limitation, we propose an innovative method that integrates graph convolutional networks with a consistency regularization framework to develop a dynamic graph consistency approach. This method imposes channel-level constraints across different decoders by leveraging high-level features within the network. Furthermore, we introduce a novel self-contrast learning strategy, which performs image-level comparison within the same batch and engages in pixel-level contrast learning based on pixel positions. This approach effectively overcomes traditional contrast learning challenges related to identifying positive and negative samples, reduces computational resource consumption, and significantly improves model performance. Our experimental evaluation on three distinct medical image segmentation datasets indicates that the proposed method demonstrates superior performance across a variety of test scenarios.