mlsanityData to 14 Oct 2026

Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation

arXiv 2105.05537 ↗First posted 12 May 2021eess.IV cs.CVcited by 1,222

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Cited by 77 in State-space sequence models (S4, Mamba), but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

In the past few years, convolutional neural networks (CNNs) have achieved milestones in medical image analysis. Especially, the deep neural networks based on U-shaped architecture and skip-connections have been widely applied in a variety of medical image tasks. However, although CNN has achieved excellent performance, it cannot learn global and long-range semantic information interaction well due to the locality of the convolution operation. In this paper, we propose Swin-Unet, which is an Unet-like pure Transformer for medical image segmentation. The tokenized image patches are fed into the Transformer-based U-shaped Encoder-Decoder architecture with skip-connections for local-global semantic feature learning. Specifically, we use hierarchical Swin Transformer with shifted windows as the encoder to extract context features. And a symmetric Swin Transformer-based decoder with patch expanding layer is designed to perform the up-sampling operation to restore the spatial resolution of the feature maps. Under the direct down-sampling and up-sampling of the inputs and outputs by 4x, experiments on multi-organ and cardiac segmentation tasks demonstrate that the pure Transformer-based U-shaped Encoder-Decoder network outperforms those methods with full-convolution or the combination of transformer and convolution. The codes and trained models will be publicly available at https://github.com/HuCaoFighting/Swin-Unet.

Key facts. Cited by 1,222 papers in the map (data through 14 Oct 2026). Builds on 30 of its 39 references that are in the map. First posted on arXiv 12 May 2021.

Builds on

30 of 39 references.

References · 30

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  4. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  5. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  6. 2020End-to-End Object Detection with Transformerscited by 7,414
  7. 2020Training data-efficient image transformers & distillation through attentioncited by 4,586
  8. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,241
  9. 2016Pyramid Scene Parsing Networkcited by 2,479
  10. 2017Non-local Neural Networkscited by 2,293
  11. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
  12. 2021Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutionscited by 1,962
18 more
  1. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,558
  2. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,525
  3. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,476
  4. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,435
  5. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 959
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  7. 2021Transformer in Transformercited by 633
  8. 2020UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentationcited by 332
  9. 2021Medical Transformer: Gated Axial-Attention for Medical Image Segmentationcited by 320
  10. 2017Relation Networks for Object Detectioncited by 305
  11. 2018Attention Gated Networks: Learning to Leverage Salient Regions in Medical Imagescited by 272
  12. 2021TransBTS: Multimodal Brain Tumor Segmentation Using Transformercited by 265
  13. 2021TransFuse: Fusing Transformers and CNNs for Medical Image Segmentationcited by 258
  14. 2021CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentationcited by 227
  15. 2019CE-Net: Context Encoder Network for 2D Medical Image Segmentationcited by 220
  16. 2019Local Relation Networks for Image Recognitioncited by 210
  17. 2020Domain Adaptive Relational Reasoning for 3D Multi-Organ Segmentationcited by 48
  18. 2018RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scanscited by 41

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 29; 2022: 134; 2023: 244; 2024: 301; 2025: 310; 2026: 204.

1,222 papers.

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  1. 2021SwinIR: Image Restoration Using Swin Transformercited by 1,887
  2. 2022Activating More Pixels in Image Super-Resolution Transformercited by 438
  3. 2021Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysiscited by 422
  4. 2024VM-UNet: Vision Mamba UNet for Medical Image Segmentationcited by 287
  5. 2023Customized Segment Anything Model for Medical Image Segmentationcited by 280
  6. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Surveycited by 273
  7. 2022Transformers in Medical Imaging: A Surveycited by 245
  8. 2024nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentationcited by 240
  9. 2023Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancementcited by 231
  10. 2021UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformercited by 197
  11. 2022Medical Image Segmentation Review: The success of U-Netcited by 191
  12. 2022UNeXt: MLP-based Rapid Medical Image Segmentation Networkcited by 185
38 more
  1. 2021nnFormer: Interleaved Transformer for Volumetric Segmentationcited by 180
  2. 2024Swin-UMamba: Mamba-based UNet with ImageNet-based pretrainingcited by 179
  3. 2021UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagerycited by 167
  4. 2023MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentationcited by 162
  5. 2022Efficient Long-Range Attention Network for Image Super-resolutioncited by 151
  6. 2024Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentationcited by 133
  7. 2023Transformer-Based Visual Segmentation: A Surveycited by 124
  8. 2022UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentationcited by 112
  9. 2022LViT: Language meets Vision Transformer in Medical Image Segmentationcited by 111
  10. 2024BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at oncecited by 111
  11. 2021Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstructioncited by 110
  12. 2021Recent advances and clinical applications of deep learning in medical image analysiscited by 104
  13. 2021MISSFormer: An Effective Medical Image Segmentation Transformercited by 103
  14. 2021Semi-Supervised Medical Image Segmentation via Cross Teaching between CNN and Transformercited by 100
  15. 2021Mixed Transformer U-Net For Medical Image Segmentationcited by 95
  16. 2022HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentationcited by 95
  17. 20223D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentationcited by 94
  18. 2022Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesiscited by 86
  19. 2021LeViT-UNet: Make Faster Encoders with Transformer for Medical Image Segmentationcited by 85
  20. 2022Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restorationcited by 73
  21. 2023Semantic Segmentation using Vision Transformers: A surveycited by 73
  22. 2022MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstructioncited by 72
  23. 2022Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imagingcited by 66
  24. 2023HAT: Hybrid Attention Transformer for Image Restorationcited by 66
  25. 2023A Comprehensive Survey on Segment Anything Model for Vision and Beyondcited by 64
  26. 2024EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentationcited by 64
  27. 2024SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentationcited by 64
  28. 2023Large-Vocabulary Segmentation for Medical Images with Text Promptscited by 62
  29. 2023Large AI Models in Health Informatics: Applications, Challenges, and the Futurecited by 61
  30. 2021TransAttUnet: Multi-level Attention-guided U-Net with Transformer for Medical Image Segmentationcited by 60
  31. 2024MiM-ISTD: Mamba-in-Mamba for Efficient Infrared Small Target Detectioncited by 60
  32. 2022MALUNet: A Multi-Attention and Light-weight UNet for Skin Lesion Segmentationcited by 59
  33. 2024State Space Model for New-Generation Network Alternative to Transformers: A Surveycited by 59
  34. 2023Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Modelcited by 56
  35. 2024Vision Mamba: A Comprehensive Survey and Taxonomycited by 54
  36. 2023EGE-UNet: an Efficient Group Enhanced UNet for skin lesion segmentationcited by 53
  37. 2022Reference-based Image Super-Resolution with Deformable Attention Transformercited by 51
  38. 2021AFTer-UNet: Axial Fusion Transformer UNet for Medical Image Segmentationcited by 48

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