Accurate medical image segmentation is critical for disease quantification and treatment evaluation. While traditional U-Net architectures and their transformer-integrated variants excel in automated segmentation tasks. Existing models also struggle with parameter efficiency and computational complexity, often due to the extensive use of Transformers. However, they lack the ability to harness the image’s intrinsic position and channel features. Research employing Dual Attention mechanisms of position and channel have not been specifically optimized for the high-detail demands of medical images. To address these issues, this study proposes a novel deep medical image segmentation framework, called DA-TransUNet, aiming to integrate the Transformer and dual attention block (DA-Block) into the traditional U-shaped architecture. Also, DA-TransUNet tailored for the high-detail requirements of medical images, optimizes the intermittent channels of Dual Attention (DA) and employs DA in each skip-connection to effectively filter out irrelevant information. This integration significantly enhances the model’s capability to extract features, thereby improving the performance of medical image segmentation. DA-TransUNet is validated in medical image segmentation tasks, consistently outperforming state-of-the-art techniques across 5 datasets. In summary, DA-TransUNet has made significant strides in medical image segmentation, offering new insights into existing techniques. It strengthens model performance from the perspective of image features, thereby advancing the development of high-precision automated medical image diagnosis. The codes and parameters of our model will be publicly available at https://github.com/SUN-1024/DA-TransUnet.

Advances in deep learning have revolutionized medical image segmentation, facilitating the precise delineation of complex anatomical structures. The scarcity of annotated training samples remains a significant bottleneck. To tackle the data limitation, federated learning (FL) offers the promise of pooling data from multiple healthcare institutions. However, as models grow larger, the increase in communication costs restricts FL to fewer nodes, which constrains the volume of data. This situation necessitates the simultaneous achievement of model lightweighting. To address this problem, this study proposes FKD-Med, a novel framework that integrates FL for privacy-sensitive data amalgamation across multiple healthcare institutions, and uses knowledge distillation (KD) to enhance communication efficiency. The "Med" in FKD-Med refers to medical application computational problems. Our principal contributions encompass the design of an open-source framework that seamlessly blends FL and KD, rendering it applicable to a broad spectrum of medical informatics tasks. Our approach substantially augments the computational data volume, thereby boosting both communication efficiency and training throughput. Tested on two datasets of medical image segmentation using TransUNet and ResUNet as teacher models, FKD-Med achieves data privacy, lowers communication costs, and increases accuracy. The parameters of the student models were reduced to 1/127 and 1/1027 of those in the teacher models. Additionally, the models subjected to KD exhibited accuracy improvements of 0.25%, 0.43%, 1.35%, and 1.46% respectively, given the same parameter volume. This positions FKD-Med not only as a pivotal tool for multi-institutional medical research but also as a versatile platform adaptable to a wide array of real-world medical engineering applications. The code is publicly available at https://github.com/SUN-1024/FKD-Med.