mlsanityData to 14 Oct 2026

High-Resolution Swin Transformer for Automatic Medical Image Segmentation

arXiv 2207.11553 ↗First posted 23 Jul 2022cs.CV cs.AIcited by 8

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Abstract (arXiv)

The Resolution of feature maps is critical for medical image segmentation. Most of the existing Transformer-based networks for medical image segmentation are U-Net-like architecture that contains an encoder that utilizes a sequence of Transformer blocks to convert the input medical image from high-resolution representation into low-resolution feature maps and a decoder that gradually recovers the high-resolution representation from low-resolution feature maps. Unlike previous studies, in this paper, we utilize the network design style from the High-Resolution Network (HRNet), replace the convolutional layers with Transformer blocks, and continuously exchange information from the different resolution feature maps that are generated by Transformer blocks. The newly Transformer-based network presented in this paper is denoted as High-Resolution Swin Transformer Network (HRSTNet). Extensive experiments illustrate that HRSTNet can achieve comparable performance with the state-of-the-art Transformer-based U-Net-like architecture on Brain Tumor Segmentation(BraTS) 2021 and the liver dataset from Medical Segmentation Decathlon. The code of HRSTNet will be publicly available at https://github.com/auroua/HRSTNet.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 37 of its 59 references that are in the map. First posted on arXiv 23 Jul 2022.

Builds on

37 of 59 references.

References · 37

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2020Language Models are Few-Shot Learnerscited by 32,419
  4. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  5. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  6. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  7. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  8. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,403
  9. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,968
  10. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  11. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 5,045
  12. 2020Training data-efficient image transformers & distillation through attentioncited by 4,586
25 more
  1. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,940
  2. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
  3. 2021Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutionscited by 1,962
  4. 2021An Empirical Study of Training Self-Supervised Vision Transformerscited by 1,616
  5. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,476
  6. 2019Deep High-Resolution Representation Learning for Visual Recognitioncited by 1,420
  7. 2020Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformerscited by 1,369
  8. 2021Video Swin Transformercited by 1,091
  9. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 959
  10. 2020Pre-Trained Image Processing Transformercited by 893
  11. 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 830
  12. 2021The Medical Segmentation Decathloncited by 628
  13. 2021The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classificationcited by 518
  14. 2019A large annotated medical image dataset for the development and evaluation of segmentation algorithmscited by 373
  15. 2021An End-to-End Transformer Model for 3D Object Detectioncited by 329
  16. 2021TransBTS: Multimodal Brain Tumor Segmentation Using Transformercited by 265
  17. 2021Voxel Transformer for 3D Object Detectioncited by 247
  18. 2021CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentationcited by 227
  19. 2015DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentationcited by 159
  20. 2021Multi-Scale High-Resolution Vision Transformer for Semantic Segmentationcited by 92
  21. 2020Toward Transformer-Based Object Detectioncited by 84
  22. 2021U-Net Transformer: Self and Cross Attention for Medical Image Segmentationcited by 81
  23. 2021Optimized U-Net for Brain Tumor Segmentationcited by 40
  24. 2021Extending nn-UNet for brain tumor segmentationcited by 36
  25. 2018Cascaded V-Net using ROI masks for brain tumor segmentationcited by 26

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 2; 2025: 2; 2026: 2.

8 papers.

  1. 2025Primus: Enforcing Attention Usage for 3D Medical Image Segmentationcited by 24
  2. 2024CIS-UNet: Multi-Class Segmentation of the Aorta in Computed Tomography Angiography via Context-Aware Shifted Window Self-Attentioncited by 5
  3. 2023Multiscale Progressive Text Prompt Network for Medical Image Segmentationcited by 3
  4. 2023Brain Tumor Segmentation Based on Deep Learning, Attention Mechanisms, and Energy-Based Uncertainty Predictioncited by 2
  5. 2025Attention Xception UNet (AXUNet): A Novel Combination of CNN and Self-Attention for Brain Tumor Segmentationcited by 1
  6. 2024Cross Feature Fusion of Fundus Image and Generated Lesion Map for Referable Diabetic Retinopathy Classificationcited by 0
  7. 2026Efficient Neural Architectures for Real-Time ECG Interpretation on Limited Hardwarecited by 0
  8. 2026Does DCGAN-Based Synthetic Augmentation Improve Brain Tumor MRI Classification? An Empirical Studycited by 0