In recent years, U‐Net and its variants have gained widespread use in medical image
segmentation. One key aspect of U‐Net's design is the skip connection, facilitating the …

Background The use of U-Net and its variations has led to significant advancements in
medical image segmentation. However, the encoder-decoder structures of these models …

Thanks to the better performance of U-Net in medical segmentation, many U-Net variants
have emerged one after another, but U-Net has the non-negligible drawback that it cannot …

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

Accurate medical image segmentation is of great significance for subsequent diagnosis and
analysis. The acquisition of multi-scale information plays an important role in segmenting …

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 …

Medical image segmentation's accuracy is crucial for clinical analysis and diagnosis.
Despite progress with U-Net-inspired models, they often underuse multi-scale convolutional …

CNNs and Transformers have significantly advanced the domain of medical image
segmentation. The integration of their strengths facilitates rich feature extraction but also …

Most recent semantic segmentation methods adopt a U-Net framework with an encoder-
decoder architecture. It is still challenging for U-Net with a simple skip connection scheme to …

In the last decade, deep neural networks have been widely applied to medical image
segmentation, achieving good results in computer-aided diagnosis tasks etc. However, the …