Dilated-UNet: A Fast and Accurate Medical Image Segmentation

This paper introduces DilatedUNet, 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

Overview

Figure 1. The overall architecture of Dilated-Unet.

References

• Swin-Unet
• Neighborhood Attention Transformers

Citation

@article{saadati2023dilated,
  title={Dilated-UNet: A Fast and Accurate Medical Image Segmentation Approach using a Dilated Transformer and U-Net Architecture},
  author={Saadati, Davoud and Manzari, Omid Nejati and Mirzakuchaki, Sattar},
  journal={arXiv preprint arXiv:2304.11450},
  year={2023}
}