mlsanityData to 7 Oct 2026

Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

arXiv 2201.01266 ↗First posted 4 Jan 2022eess.IV cs.CV cs.LGcited by 823

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Cited by 53 in State-space sequence models (S4, Mamba), but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Semantic segmentation of brain tumors is a fundamental medical image analysis task involving multiple MRI imaging modalities that can assist clinicians in diagnosing the patient and successively studying the progression of the malignant entity. In recent years, Fully Convolutional Neural Networks (FCNNs) approaches have become the de facto standard for 3D medical image segmentation. The popular "U-shaped" network architecture has achieved state-of-the-art performance benchmarks on different 2D and 3D semantic segmentation tasks and across various imaging modalities. However, due to the limited kernel size of convolution layers in FCNNs, their performance of modeling long-range information is sub-optimal, and this can lead to deficiencies in the segmentation of tumors with variable sizes. On the other hand, transformer models have demonstrated excellent capabilities in capturing such long-range information in multiple domains, including natural language processing and computer vision. Inspired by the success of vision transformers and their variants, we propose a novel segmentation model termed Swin UNEt TRansformers (Swin UNETR). Specifically, the task of 3D brain tumor semantic segmentation is reformulated as a sequence to sequence prediction problem wherein multi-modal input data is projected into a 1D sequence of embedding and used as an input to a hierarchical Swin transformer as the encoder. The swin transformer encoder extracts features at five different resolutions by utilizing shifted windows for computing self-attention and is connected to an FCNN-based decoder at each resolution via skip connections. We have participated in BraTS 2021 segmentation challenge, and our proposed model ranks among the top-performing approaches in the validation phase. Code: https://monai.io/research/swin-unetr

Key facts. Cited by 823 papers in the map (data through 7 Oct 2026). Builds on 29 of its 41 references that are in the map. First posted on arXiv 4 Jan 2022.

Builds on

29 of 41 references.

References · 29

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  4. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  5. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,893
  6. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  7. 2021BEiT: BERT Pre-Training of Image Transformerscited by 2,360
  8. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
  9. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,552
  10. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  11. 2020Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformerscited by 1,368
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17 more
  1. 2021Video Swin Transformercited by 1,090
  2. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 954
  3. 2021Do Vision Transformers See Like Convolutional Neural Networks?cited by 671
  4. 2021The Medical Segmentation Decathloncited by 620
  5. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 508
  6. 2021The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classificationcited by 507
  7. 2021Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysiscited by 418
  8. 2019A large annotated medical image dataset for the development and evaluation of segmentation algorithmscited by 371
  9. 20183D MRI brain tumor segmentation using autoencoder regularizationcited by 324
  10. 2021TransBTS: Multimodal Brain Tumor Segmentation Using Transformercited by 265
  11. 2016Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentationcited by 263
  12. 2021CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentationcited by 227
  13. 2020nnU-Net for Brain Tumor Segmentationcited by 103
  14. 2017Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentationcited by 58
  15. 20193D Whole Brain Segmentation using Spatially Localized Atlas Network Tilescited by 50
  16. 20193D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRIcited by 28
  17. 2020Robust Semantic Segmentation of Brain Tumor Regions from 3D MRIscited by 12

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 46; 2023: 118; 2024: 224; 2025: 249; 2026: 186.

823 papers.

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  1. 2023Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentationcited by 485
  2. 2024U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentationcited by 412
  3. 2022AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentationcited by 347
  4. 2024SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentationcited by 266
  5. 2022Transformers in Medical Imaging: A Surveycited by 245
  6. 2024nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentationcited by 235
  7. 2022Medical Image Segmentation Review: The success of U-Netcited by 190
  8. 2024Swin-UMamba: Mamba-based UNet with ImageNet-based pretrainingcited by 179
  9. 2024U-KAN Makes Strong Backbone for Medical Image Segmentation and Generationcited by 176
  10. 2023MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentationcited by 161
  11. 2024Medical SAM 2: Segment medical images as video via Segment Anything Model 2cited by 151
  12. 2023MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformercited by 124
38 more
  1. 2022UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentationcited by 112
  2. 2023CellViT: Vision Transformers for Precise Cell Segmentation and Classificationcited by 108
  3. 2024BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at oncecited by 107
  4. 2023STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-trainingcited by 96
  5. 20223D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentationcited by 94
  6. 2023SegVol: Universal and Interactive Volumetric Medical Image Segmentationcited by 86
  7. 2024Mamba-ND: Selective State Space Modeling for Multi-Dimensional Datacited by 84
  8. 2023Foundation Model for Endoscopy Video Analysis via Large-scale Self-supervised Pre-traincited by 75
  9. 2022Masked Image Modeling Advances 3D Medical Image Analysiscited by 71
  10. 2024Towards Generalizable Tumor Synthesiscited by 65
  11. 2024AbdomenAtlas: A Large-Scale, Detailed-Annotated, & Multi-Center Dataset for Efficient Transfer Learning and Open Algorithmic Benchmarkingcited by 64
  12. 2022Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysiscited by 62
  13. 2023Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Reviewcited by 62
  14. 2023Large-Vocabulary Segmentation for Medical Images with Text Promptscited by 60
  15. 2024MiM-ISTD: Mamba-in-Mamba for Efficient Infrared Small Target Detectioncited by 59
  16. 2023Label-Free Liver Tumor Segmentationcited by 56
  17. 2024A Comprehensive Survey of Foundation Models in Medicinecited by 56
  18. 2024LightM-UNet: Mamba Assists in Lightweight UNet for Medical Image Segmentationcited by 54
  19. 2023How to Efficiently Adapt Large Segmentation Model(SAM) to Medical Imagescited by 53
  20. 2024Vision Mamba: A Comprehensive Survey and Taxonomycited by 53
  21. 2023The TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiographycited by 48
  22. 2022A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmarkcited by 45
  23. 2024nnMamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Modelcited by 45
  24. 2024UltraLight VM-UNet: Parallel Vision Mamba Significantly Reduces Parameters for Skin Lesion Segmentationcited by 45
  25. 2023Continual Learning for Abdominal Multi-Organ and Tumor Segmentationcited by 43
  26. 2022Transformers in Medical Image Analysis: A Reviewcited by 41
  27. 2023Diff-UNet: A Diffusion Embedded Network for Volumetric Segmentationcited by 41
  28. 2023One-Prompt to Segment All Medical Imagescited by 38
  29. 2023SwiFT: Swin 4D fMRI Transformercited by 36
  30. 2024BrainSegFounder: Towards 3D Foundation Models for Neuroimage Segmentationcited by 36
  31. 2023SAM3D: Segment Anything Model in Volumetric Medical Imagescited by 34
  32. 2024LKM-UNet: Large Kernel Vision Mamba UNet for Medical Image Segmentationcited by 34
  33. 2024Large-Scale 3D Medical Image Pre-training with Geometric Context Priorscited by 34
  34. 2023Ariadne's Thread:Using Text Prompts to Improve Segmentation of Infected Areas from Chest X-ray imagescited by 33
  35. 2024Revisiting MAE pre-training for 3D medical image segmentationcited by 33
  36. 2022Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectivescited by 32
  37. 2023Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentationcited by 32
  38. 2022The Fully Convolutional Transformer for Medical Image Segmentationcited by 31

50 most cited of 823 All 823 (JSON)