Learning Multi-axis Representation in Frequency Domain for Medical Image
  Segmentation
Jiacheng Ruan Jingsheng Gao Mingye Xie Suncheng Xiang
Recently, Visual Transformer (ViT) has been extensively used in medical image
segmentation (MIS) due to applying self-attention mechanism in the spatial
domain to modeling global knowledge. However, many studies have focused on
improving models in the spatial domain while neglecting the importance of
frequency domain information. Therefore, we propose Multi-axis External Weights
UNet (MEW-UNet) based on the U-shape architecture by replacing self-attention
in ViT with our Multi-axis External Weights block. Specifically, our block
performs a Fourier transform on the three axes of the input features and
assigns the external weight in the frequency domain, which is generated by our
External Weights Generator. Then, an inverse Fourier transform is performed to
change the features back to the spatial domain. We evaluate our model on four
datasets, including Synapse, ACDC, ISIC17 and ISIC18 datasets, and our approach
demonstrates competitive performance, owing to its effective utilization of
frequency domain information.