DCA-Net: Data-Driven Collaborative Assistance Network for Semi-supervised Medical Segmentation

In this paper, we focus on the empirical alignment challenge between labeled and unlabeled data in semi-supervised medical image segmentation. When labeled and unlabeled data are poorly aligned, the network struggles to fully leverage knowledge from the labeled data. To address this, we propose an efficient and streamlined approach called "DCA-Net," which integrates a frequency-domain data augmentation module Style Transfer Module (STM) and Bidirectional Copy-Paste (BCP) to effectively reduce the distribution gap between labeled and unlabeled data. Additionally, we combine knowledge distillation with semi-supervised learning to encourage deeper feature learning and more stable model behavior. Experiments with DCA-Net on the LA and ACDC datasets achieve state-of-the-art (SOTA) results.