mlsanityData to 7 Oct 2026

Learning Multi-axis Representation in Frequency Domain for Medical Image Segmentation

arXiv 2312.17030 ↗First posted 28 Dec 2023eess.IV cs.CVcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 30 of its 46 references that are in the map. First posted on arXiv 28 Dec 2023.

Builds on

30 of 46 references.

References · 30

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  4. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  5. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,773
  6. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
  7. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,552
  8. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,518
  9. 2018Group Normalizationcited by 1,514
  10. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,420
  11. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,218
  12. 2019Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)cited by 657
18 more
  1. 2016Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentationcited by 263
  2. 2021TransFuse: Fusing Transformers and CNNs for Medical Image Segmentationcited by 258
  3. 2021Global Filter Networks for Image Classificationcited by 257
  4. 2021UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformercited by 197
  5. 2021Shallow Attention Network for Polyp Segmentationcited by 107
  6. 2021Mixed Transformer U-Net For Medical Image Segmentationcited by 95
  7. 2018HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentationcited by 66
  8. 2022MALUNet: A Multi-Attention and Light-weight UNet for Skin Lesion Segmentationcited by 59
  9. 2023EGE-UNet: an Efficient Group Enhanced UNet for skin lesion segmentationcited by 53
  10. 2021Convolution-Free Medical Image Segmentation using Transformerscited by 49
  11. 2017ISIC 2017 - Skin Lesion Analysis Towards Melanoma Detectioncited by 34
  12. 2021Less is More: Learning from Synthetic Data with Fine-grained Attributes for Person Re-Identificationcited by 19
  13. 2023LAMM: Label Alignment for Multi-Modal Prompt Learningcited by 15
  14. 2021Learning from Self-Discrepancy via Multiple Co-teaching for Cross-Domain Person Re-Identificationcited by 11
  15. 2022TVConv: Efficient Translation Variant Convolution for Layout-aware Visual Processingcited by 7
  16. 2021Rethinking Person Re-Identification via Semantic-Based Pretrainingcited by 6
  17. 2023Colo-SCRL: Self-Supervised Contrastive Representation Learning for Colonoscopic Video Retrievalcited by 6
  18. 2023AutoKary2022: A Large-Scale Densely Annotated Dataset for Chromosome Instance Segmentationcited by 5

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Cited by year

Citing papers per year: 2024: 1; 2025: 2.

3 papers.

  1. 2024VM-UNet: Vision Mamba UNet for Medical Image Segmentationcited by 286
  2. 2025FMD-TransUNet: Abdominal Multi-Organ Segmentation Based on Frequency Domain Multi-Axis Representation Learning and Dual Attention Mechanismscited by 0
  3. 2025BALR-SAM: Boundary-Aware Low-Rank Adaptation of SAM for Resource-Efficient Medical Image Segmentationcited by 0