Medical image segmentation is of great help to clinical practice. However, due to the high annotation costs and substantial time requirements for medical images, fully supervised medical image segmentation models often suffer from insufficient data volume,leading to poor performance. To tackle the annotation challenge of medical image segmentation, we introduce a semi-supervised learning algorithm called vatMatch, which relies on strong-weak consistency perturbation. It leverages partially annotated data to guide the training on unlabeled data and can achieve results comparable to fully supervised learning. Additionally, due to the uneven distribution of feature information across various channels in medical images, the existing mainstream methods based on Transformers are not effective in extracting feature information from individual image channels, and typically incur significant computational resource usage during the feature extraction process. To extract more feature information across different channels using minimal computational resources, we propose a Pixel Fusion Module (PFA) and an Adaptive Large Kernel Convolution (ALK) to enhance the ability of feature information extraction. By integrating PFA, ALK, and vatMatch, we propose a detail-enhanced attention network called VATBoost-Net, which not only addresses the annotation challenge but also enhances the capability of extracting image information features. Experimental results demonstrate the effectiveness of VATBoost-Net. Especially, our method outperforms the current state-of-the-art (SOTA) semi-supervised learning segmentation method UniMatch on the ACDC dataset.