MEW-UNet: Multi-axis representation learning in frequency domain for medical image segmentation
This work addresses medical image segmentation by incorporating frequency-domain information, offering an incremental improvement over existing methods.
The paper tackles medical image segmentation by introducing a Multi-axis External Weights UNet (MEW-UNet) that replaces self-attention in Vision Transformers with a frequency-domain block, achieving state-of-the-art results such as a 10.15mm improvement in HD95 on the Synapse dataset.
Recently, Visual Transformer (ViT) has been widely used in various fields of computer vision due to applying self-attention mechanism in the spatial domain to modeling global knowledge. Especially in medical image segmentation (MIS), many works are devoted to combining ViT and CNN, and even some works directly utilize pure ViT-based models. However, recent works improved models in the aspect of spatial domain while ignoring the importance of frequency domain information. Therefore, we propose Multi-axis External Weights UNet (MEW-UNet) for MIS 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 feature and assigns the external weight in the frequency domain, which is generated by our 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 and achieve state-of-the-art performances. In particular, on the Synapse dataset, our method outperforms MT-UNet by 10.15mm in terms of HD95. Code is available at https://github.com/JCruan519/MEW-UNet.