Semi-supervised contextual cognitive augmentation-based cross-teaching network for multiclass medical image segmentation

The application of medical image segmentation technology enables accurate localization of human tissues, providing doctors with a reliable foundation for diagnosis. While deep learning methods have proven effective in this task, most current approaches rely on a single prediction framework, which overlooks Edge semantic features and results in flawed texture features. Moreover, existing supervised methods face challenges due to limited availability of high-quality annotations in the field of medical imaging. In this article, a Semi-supervised Contextual Cognitive Augmentation-based Cross-teaching Network is proposed. A Contextual Cognitive Enhancement Module is introduced consisting of two components: data augmentation and information extraction. The data augmentation component provides multi-level data distribution by incorporating diverse perturbation strategies such as Discrete Cosine Transform and Gaussian noise. The information extraction component employs the Comprehensive Information Extraction module, which consists of Global Perception Information Extraction module and Multi-channel Information Extraction module to extract perceptual information from images and enhance interaction between image channels, respectively. Additionally, a cross-teaching strategy is adopted and a hybrid loss function is utilized to encourage knowledge sharing among the networks, leveraging the advantages of dual networks for improved performance. Experimental results demonstrate significant enhancements in multiclass medical image segmentation compared to several state-of-the-art single-framework networks. The application of medical image segmentation technology facilitates precise tissue localization, aiding doctors in accurate diagnoses. To address limitations of current methods, we propose a Semi-supervised Contextual Cognitive Augmentation-based Cross-teaching Network. This network incorporates a Contextual Cognitive Enhancement Module, employing data augmentation techniques and information extraction mechanisms to improve segmentation accuracy. By utilizing a cross-teaching strategy and hybrid loss function, our approach encourages knowledge sharing between networks, leading to substantial improvements in multiclass medical image segmentation over existing single-framework networks, as demonstrated in experimental results. image