mlsanityData to 7 Oct 2026

nnFormer: Interleaved Transformer for Volumetric Segmentation

arXiv 2109.03201 ↗First posted 7 Sep 2021cs.CVcited by 179

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Abstract (arXiv)

Transformer, the model of choice for natural language processing, has drawn scant attention from the medical imaging community. Given the ability to exploit long-term dependencies, transformers are promising to help atypical convolutional neural networks to overcome their inherent shortcomings of spatial inductive bias. However, most of recently proposed transformer-based segmentation approaches simply treated transformers as assisted modules to help encode global context into convolutional representations. To address this issue, we introduce nnFormer, a 3D transformer for volumetric medical image segmentation. nnFormer not only exploits the combination of interleaved convolution and self-attention operations, but also introduces local and global volume-based self-attention mechanism to learn volume representations. Moreover, nnFormer proposes to use skip attention to replace the traditional concatenation/summation operations in skip connections in U-Net like architecture. Experiments show that nnFormer significantly outperforms previous transformer-based counterparts by large margins on three public datasets. Compared to nnUNet, nnFormer produces significantly lower HD95 and comparable DSC results. Furthermore, we show that nnFormer and nnUNet are highly complementary to each other in model ensembling.

Key facts. Cited by 179 papers in the map (data through 7 Oct 2026). Builds on 31 of its 45 references that are in the map. First posted on arXiv 7 Sep 2021.

Builds on

31 of 45 references.

References · 31

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  4. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  5. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  6. 2020End-to-End Object Detection with Transformerscited by 7,384
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  10. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
  11. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,552
  12. 2020Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformerscited by 1,368
19 more
  1. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,218
  2. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 954
  3. 2021The Medical Segmentation Decathloncited by 620
  4. 2017Understanding the Effective Receptive Field in Deep Convolutional Neural Networkscited by 549
  5. 2021Medical Transformer: Gated Axial-Attention for Medical Image Segmentationcited by 319
  6. 2021TransBTS: Multimodal Brain Tumor Segmentation Using Transformercited by 265
  7. 2021TransFuse: Fusing Transformers and CNNs for Medical Image Segmentationcited by 258
  8. 2021CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentationcited by 227
  9. 2017Receptive Field Block Net for Accurate and Fast Object Detectioncited by 140
  10. 2021DS-TransUNet:Dual Swin Transformer U-Net for Medical Image Segmentationcited by 126
  11. 2021Are Convolutional Neural Networks or Transformers more like human vision?cited by 109
  12. 2021LeViT-UNet: Make Faster Encoders with Transformer for Medical Image Segmentationcited by 85
  13. 2021TransAttUnet: Multi-level Attention-guided U-Net with Transformer for Medical Image Segmentationcited by 60
  14. 2021Medical Image Segmentation Using Squeeze-and-Expansion Transformerscited by 56
  15. 2021Convolution-Free Medical Image Segmentation using Transformerscited by 49
  16. 2021TransClaw U-Net: Claw U-Net with Transformers for Medical Image Segmentationcited by 41
  17. 2021ConvNets vs. Transformers: Whose Visual Representations are More Transferable?cited by 35
  18. 2021More than Encoder: Introducing Transformer Decoder to Upsamplecited by 26
  19. 2021SpecTr: Spectral Transformer for Hyperspectral Pathology Image Segmentationcited by 23

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 9; 2023: 25; 2024: 65; 2025: 50; 2026: 28.

179 papers.

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  1. 2024U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentationcited by 412
  2. 2024Swin-UMamba: Mamba-based UNet with ImageNet-based pretrainingcited by 179
  3. 2022UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentationcited by 112
  4. 2024BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at oncecited by 107
  5. 2023MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentationcited by 82
  6. 2023Large-Vocabulary Segmentation for Medical Images with Text Promptscited by 60
  7. 2024Vision Mamba: A Comprehensive Survey and Taxonomycited by 53
  8. 2024nnMamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Modelcited by 45
  9. 2022Transformers in Medical Image Analysis: A Reviewcited by 41
  10. 2024LKM-UNet: Large Kernel Vision Mamba UNet for Medical Image Segmentationcited by 34
  11. 2023Artificial General Intelligence for Medical Imaging Analysiscited by 32
  12. 2024Weak-Mamba-UNet: Visual Mamba Makes CNN and ViT Work Better for Scribble-based Medical Image Segmentationcited by 29
38 more
  1. 2024Foundation Models for Biomedical Image Segmentation: A Surveycited by 28
  2. 2022Transformer Meets Boundary Value Inverse Problemscited by 22
  3. 2023A Recent Survey of Vision Transformers for Medical Image Segmentationcited by 22
  4. 2025Primus: Enforcing Attention Usage for 3D Medical Image Segmentationcited by 22
  5. 20233D TransUNet: Advancing Medical Image Segmentation through Vision Transformerscited by 20
  6. 2024Semi-Mamba-UNet: Pixel-Level Contrastive and Pixel-Level Cross-Supervised Visual Mamba-based UNet for Semi-Supervised Medical Image Segmentationcited by 20
  7. 2023AutoProSAM: Automated Prompting SAM for 3D Multi-Organ Segmentationcited by 19
  8. 2023Promise:Prompt-driven 3D Medical Image Segmentation Using Pretrained Image Foundation Modelscited by 18
  9. 2024Rethinking Abdominal Organ Segmentation (RAOS) in the clinical scenario: A robustness evaluation benchmark with challenging casescited by 18
  10. 2024SDR-Former: A Siamese Dual-Resolution Transformer for Liver Lesion Classification Using 3D Multi-Phase Imagingcited by 17
  11. 2024T-Mamba: A unified framework with Long-Range Dependency in dual-domain for 2D & 3D Tooth Segmentationcited by 17
  12. 2022UNetFormer: A Unified Vision Transformer Model and Pre-Training Framework for 3D Medical Image Segmentationcited by 16
  13. 2025Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentationcited by 16
  14. 2021Multi-domain Integrative Swin Transformer network for Sparse-View Tomographic Reconstructioncited by 15
  15. 2024UU-Mamba: Uncertainty-aware U-Mamba for Cardiac Image Segmentationcited by 15
  16. 2022BATFormer: Towards Boundary-Aware Lightweight Transformer for Efficient Medical Image Segmentationcited by 14
  17. 2024SMAFormer: Synergistic Multi-Attention Transformer for Medical Image Segmentationcited by 14
  18. 2024MemWarp: Discontinuity-Preserving Cardiac Registration with Memorized Anatomical Filterscited by 13
  19. 2022Evidence fusion with contextual discounting for multi-modality medical image segmentationcited by 12
  20. 2024Rotate to Scan: UNet-like Mamba with Triplet SSM Module for Medical Image Segmentationcited by 12
  21. 2024Towards a Comprehensive, Efficient and Promptable Anatomic Structure Segmentation Model using 3D Whole-body CT Scanscited by 10
  22. 2024Multimodal Data Integration for Precision Oncology: Challenges and Future Directionscited by 10
  23. 2025Diffusion-empowered AutoPrompt MedSAMcited by 10
  24. 2025A Comprehensive Review of U-Net and Its Variants: Advances and Applications in Medical Image Segmentationcited by 10
  25. 2022Graph Reasoning Transformer for Image Parsingcited by 9
  26. 2024D-Net: Dynamic Large Kernel with Dynamic Feature Fusion for Volumetric Medical Image Segmentationcited by 9
  27. 2024A Flexible 2.5D Medical Image Segmentation Approach with In-Slice and Cross-Slice Attentioncited by 9
  28. 2024Large Scale MRI Collection and Segmentation of Cirrhotic Livercited by 9
  29. 2024TBConvL-Net: A Hybrid Deep Learning Architecture for Robust Medical Image Segmentationcited by 8
  30. 2023Graph Convolution Based Cross-Network Multi-Scale Feature Fusion for Deep Vessel Segmentationcited by 7
  31. 2023GeoSAM: Fine-tuning SAM with Multi-Modal Prompts for Mobility Infrastructure Segmentationcited by 7
  32. 2024BRAU-Net++: U-Shaped Hybrid CNN-Transformer Network for Medical Image Segmentationcited by 7
  33. 2024MedUniSeg: 2D and 3D Medical Image Segmentation via a Prompt-driven Universal Modelcited by 7
  34. 2025Towards Universal Text-driven CT Image Segmentationcited by 7
  35. 2023Attention-Map Augmentation for Hypercomplex Breast Cancer Classificationcited by 6
  36. 2023SeUNet-Trans: A Simple yet Effective UNet-Transformer Model for Medical Image Segmentationcited by 6
  37. 2023Slide-SAM: Medical SAM Meets Sliding Windowcited by 6
  38. 2024PMFSNet: Polarized Multi-scale Feature Self-attention Network For Lightweight Medical Image Segmentationcited by 6

50 most cited of 179 All 179 (JSON)