  Medical image segmentation is crucial for the development of computer-aided
diagnostic and therapeutic systems, but still faces numerous difficulties. In
recent years, the commonly used encoder-decoder architecture based on CNNs has
been applied effectively in medical image segmentation, but has limitations in
terms of learning global context and spatial relationships. Some researchers
have attempted to incorporate transformers into both the decoder and encoder
components, with promising results, but this approach still requires further
improvement due to its high computational complexity. This paper introduces
Dilated-UNet, which combines a Dilated Transformer block with the U-Net
architecture for accurate and fast medical image segmentation. Image patches
are transformed into tokens and fed into the U-shaped encoder-decoder
architecture, with skip-connections for local-global semantic feature learning.
The encoder uses a hierarchical Dilated Transformer with a combination of
Neighborhood Attention and Dilated Neighborhood Attention Transformer to
extract local and sparse global attention. The results of our experiments show
that Dilated-UNet outperforms other models on several challenging medical image
segmentation datasets, such as ISIC and Synapse.
