Abstract We propose Dual Cross-Attention (DCA), a simple yet effective attention module
that enhances skip-connections in U-Net-based architectures for medical image …

Current state-of-the-art medical image segmentation techniques predominantly employ the
encoder–decoder architecture. Despite its widespread use, this U-shaped framework …

Accurate image segmentation plays a crucial role in medical image analysis, yet it faces
great challenges caused by various shapes, diverse sizes, and blurry boundaries. To …

Deep learning architecture with convolutional neural network achieves outstanding success
in the field of computer vision. Where U-Net has made a great breakthrough in biomedical …

The U-Net has achieved great success in medical image segmentation. Most U-Nets follow
the encoding–decoding-decision inference path and propagate the features from encoding …

In recent years, deep learning has dominated medical image segmentation. Encoder-
decoder architectures, such as U-Net, can be used in state-of-the-art models with powerful …

Accurate medical image segmentation is critical for disease quantification and treatment
evaluation. While traditional U-Net architectures and their transformer-integrated variants …

As an important building block in automatic medical systems, image segmentation has made
great progress due to the data-driving mechanism of deep architecture. Recently, numerous …

Automatic medical image segmentation has made great progress owing to powerful deep
representation learning. Inspired by the success of self-attention mechanism in transformer …

Medical image segmentation remains particularly challenging for complex and low-contrast
anatomical structures. In this paper, we introduce the U-Transformer network, which …