Data to 7 Oct 2026

TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

arXiv 2102.04306 ↗First posted 8 Feb 2021cs.CVcited by 1,552

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Cited by 73 in State-space sequence models (S4, Mamba), but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Medical image segmentation is an essential prerequisite for developing healthcare systems, especially for disease diagnosis and treatment planning. On various medical image segmentation tasks, the u-shaped architecture, also known as U-Net, has become the de-facto standard and achieved tremendous success. However, due to the intrinsic locality of convolution operations, U-Net generally demonstrates limitations in explicitly modeling long-range dependency. Transformers, designed for sequence-to-sequence prediction, have emerged as alternative architectures with innate global self-attention mechanisms, but can result in limited localization abilities due to insufficient low-level details. In this paper, we propose TransUNet, which merits both Transformers and U-Net, as a strong alternative for medical image segmentation. On one hand, the Transformer encodes tokenized image patches from a convolution neural network (CNN) feature map as the input sequence for extracting global contexts. On the other hand, the decoder upsamples the encoded features which are then combined with the high-resolution CNN feature maps to enable precise localization. We argue that Transformers can serve as strong encoders for medical image segmentation tasks, with the combination of U-Net to enhance finer details by recovering localized spatial information. TransUNet achieves superior performances to various competing methods on different medical applications including multi-organ segmentation and cardiac segmentation. Code and models are available at https://github.com/Beckschen/TransUNet.

Key facts. Cited by 1,552 papers in the map (data through 7 Oct 2026). Builds on 17 of its 21 references that are in the map. First posted on arXiv 8 Feb 2021.

Builds on

17 of 21 references.

References · 17

2017Attention Is All You Needcited by 59,932
2015Deep Residual Learning for Image Recognitioncited by 51,809
2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
2017Non-local Neural Networkscited by 2,290
2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
2019Generating Long Sequences with Sparse Transformerscited by 1,523
2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,518
2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,420
2020Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformerscited by 1,368

5 more

2018Image Transformercited by 770
2018Attention Gated Networks: Learning to Leverage Salient Regions in Medical Imagescited by 271
2020Semi-supervised Medical Image Segmentation through Dual-task Consistencycited by 215
2020Domain Adaptive Relational Reasoning for 3D Multi-Organ Segmentationcited by 48
2017Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNetscited by 32

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 92; 2022: 211; 2023: 300; 2024: 381; 2025: 352; 2026: 216.

1,552 papers.

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2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,218
2021UNETR: Transformers for 3D Medical Image Segmentationcited by 954
2021Multiscale Vision Transformerscited by 896
2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 823
2021TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classificationcited by 549
2021Image Segmentation Using Text and Image Promptscited by 509
2023Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentationcited by 485
2021Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysiscited by 418
2024U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentationcited by 412
2021Medical Transformer: Gated Axial-Attention for Medical Image Segmentationcited by 319
2024VM-UNet: Vision Mamba UNet for Medical Image Segmentationcited by 286
2023Customized Segment Anything Model for Medical Image Segmentationcited by 277

38 more

2022Diffusion Models for Medical Image Analysis: A Comprehensive Surveycited by 269
2021TransBTS: Multimodal Brain Tumor Segmentation Using Transformercited by 265
2021TransFuse: Fusing Transformers and CNNs for Medical Image Segmentationcited by 258
2022Transformers in Medical Imaging: A Surveycited by 245
2024nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentationcited by 235
2021CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentationcited by 227
2023CLIP-Driven Universal Model for Organ Segmentation and Tumor Detectioncited by 224
2021UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformercited by 197
2022Medical Image Segmentation Review: The success of U-Netcited by 190
2022UNeXt: MLP-based Rapid Medical Image Segmentation Networkcited by 185
2022MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Modelcited by 180
2021nnFormer: Interleaved Transformer for Volumetric Segmentationcited by 179
2021ResViT: Residual vision transformers for multi-modal medical image synthesiscited by 177
2023Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Datacited by 176
2024U-KAN Makes Strong Backbone for Medical Image Segmentation and Generationcited by 176
2021UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagerycited by 166
2023MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentationcited by 161
2024Medical SAM 2: Segment medical images as video via Segment Anything Model 2cited by 151
2021Rendezvous: Attention Mechanisms for the Recognition of Surgical Action Triplets in Endoscopic Videoscited by 139
2021Visformer: The Vision-friendly Transformercited by 137
2024Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentationcited by 133
2021TransFG: A Transformer Architecture for Fine-grained Recognitioncited by 131
2021DS-TransUNet:Dual Swin Transformer U-Net for Medical Image Segmentationcited by 126
2021Transformer-Based Attention Networks for Continuous Pixel-Wise Predictioncited by 125
2023MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformercited by 124
2023Transformer-Based Visual Segmentation: A Surveycited by 124
2021A Survey of Visual Transformerscited by 117
2022UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentationcited by 112
2022LViT: Language meets Vision Transformer in Medical Image Segmentationcited by 110
2024BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at oncecited by 107
2021Recent advances and clinical applications of deep learning in medical image analysiscited by 104
2021MISSFormer: An Effective Medical Image Segmentation Transformercited by 103
2021Semi-Supervised Medical Image Segmentation via Cross Teaching between CNN and Transformercited by 99
2022Inception Transformercited by 99
2022A Generalist Framework for Panoptic Segmentation of Images and Videoscited by 99
2023STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-trainingcited by 96
2021Mixed Transformer U-Net For Medical Image Segmentationcited by 95
2022HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentationcited by 94

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