# MEW-UNet: Multi-axis representation learning in frequency domain for medical image segmentation

arXiv: https://arxiv.org/abs/2210.14007 · HTML: https://mlsanity.com/paper/2210.14007/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 19 references that are in the map.
- First posted on arXiv 25 Oct 2022.

## Builds on (most cited first, 15 of 15)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [Global Filter Networks for Image Classification](https://mlsanity.com/paper/2107.00645/) (2021)
- [TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation](https://mlsanity.com/paper/2102.08005/) (2021)
- [UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer](https://mlsanity.com/paper/2109.04335/) (2021)
- [Shallow Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2108.00882/) (2021)
- [Mixed Transformer U-Net For Medical Image Segmentation](https://mlsanity.com/paper/2111.04734/) (2021)
- [TVConv: Efficient Translation Variant Convolution for Layout-aware Visual Processing](https://mlsanity.com/paper/2203.10489/) (2022)

## Built on by (most cited first, 8 of 8)

- [VM-UNet: Vision Mamba UNet for Medical Image Segmentation](https://mlsanity.com/paper/2402.02491/) (2024)
- [SliceMamba with Neural Architecture Search for Medical Image Segmentation](https://mlsanity.com/paper/2407.08481/) (2024)
- [Merging Context Clustering with Visual State Space Models for Medical Image Segmentation](https://mlsanity.com/paper/2501.01618/) (2025)
- [SkinFormer: Learning Statistical Texture Representation with Transformer for Skin Lesion Segmentation](https://mlsanity.com/paper/2409.08652/) (2024)
- [GCA-ResUNet: Medical Image Segmentation Using Grouped Coordinate Attention](https://mlsanity.com/paper/2512.23990/) (2025)
- [Phi-SegNet: Phase-Integrated Supervision for Medical Image Segmentation](https://mlsanity.com/paper/2601.16064/) (2026)
- [DSVM-UNet : Enhancing VM-UNet with Dual Self-distillation for Medical Image Segmentation](https://mlsanity.com/paper/2601.19690/) (2026)
- [APRIL-MedSeg: A Modular Medical Image Segmentation Toolbox Embracing Modern Paradigms](https://mlsanity.com/paper/2606.30577/) (2026)

## Abstract (arXiv)

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.