mlsanityData to 7 Oct 2026

Vision Transformers in Medical Imaging: A Review

arXiv 2211.10043 ↗First posted 18 Nov 2022cs.CV cs.AIcited by 8

Not on the map. Why?

Abstract (arXiv)

Transformer, a model comprising attention-based encoder-decoder architecture, have gained prevalence in the field of natural language processing (NLP) and recently influenced the computer vision (CV) space. The similarities between computer vision and medical imaging, reviewed the question among researchers if the impact of transformers on computer vision be translated to medical imaging? In this paper, we attempt to provide a comprehensive and recent review on the application of transformers in medical imaging by; describing the transformer model comparing it with a diversity of convolutional neural networks (CNNs), detailing the transformer based approaches for medical image classification, segmentation, registration and reconstruction with a focus on the image modality, comparing the performance of state-of-the-art transformer architectures to best performing CNNs on standard medical datasets.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 107 of its 165 references that are in the map. First posted on arXiv 18 Nov 2022.

Builds on

107 of 165 references.

References · 107

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  4. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  5. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  6. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  7. 2020End-to-End Object Detection with Transformerscited by 7,384
  8. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,893
  9. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  10. 2017Squeeze-and-Excitation Networkscited by 4,763
  11. 2020Training data-efficient image transformers & distillation through attentioncited by 4,560
  12. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
38 more
  1. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,590
  2. 2022A ConvNet for the 2020scited by 3,545
  3. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,772
  4. 2018CBAM: Convolutional Block Attention Modulecited by 2,463
  5. 2017Non-local Neural Networkscited by 2,290
  6. 2017Deformable Convolutional Networkscited by 1,481
  7. 2015Show, Attend and Tell: Neural Image Caption Generation with Visual Attentioncited by 1,421
  8. 2020Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformerscited by 1,368
  9. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 954
  10. 2021CvT: Introducing Convolutions to Vision Transformerscited by 946
  11. 2022Exploring Plain Vision Transformer Backbones for Object Detectioncited by 723
  12. 2018CCNet: Criss-Cross Attention for Semantic Segmentationcited by 717
  13. 2021Do Vision Transformers See Like Convolutional Neural Networks?cited by 671
  14. 2013Speech Recognition with Deep Recurrent Neural Networkscited by 666
  15. 2019ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networkscited by 572
  16. 2021CoAtNet: Marrying Convolution and Attention for All Data Sizescited by 566
  17. 2021TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classificationcited by 549
  18. 2019Stand-Alone Self-Attention in Vision Modelscited by 542
  19. 2019Using Self-Supervised Learning Can Improve Model Robustness and Uncertaintycited by 511
  20. 2021ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biasescited by 484
  21. 2021Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysiscited by 418
  22. 2019Transfusion: Understanding Transfer Learning for Medical Imagingcited by 366
  23. 2021Bottleneck Transformers for Visual Recognitioncited by 364
  24. 2022Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learningcited by 357
  25. 2019GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyondcited by 331
  26. 2020CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentationcited by 307
  27. 2022How Do Vision Transformers Work?cited by 305
  28. 2019On the Relationship between Self-Attention and Convolutional Layerscited by 299
  29. 2022Vision Transformer with Deformable Attentioncited by 293
  30. 2020COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Imagescited by 264
  31. 2019Revisiting Self-Supervised Visual Representation Learningcited by 250
  32. 2021Understanding Robustness of Transformers for Image Classificationcited by 250
  33. 2021Mobile-Former: Bridging MobileNet and Transformercited by 234
  34. 2021CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentationcited by 227
  35. 2021Scaling Local Self-Attention for Parameter Efficient Visual Backbonescited by 220
  36. 2018End-to-End Dense Video Captioning with Masked Transformercited by 214
  37. 2016Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challengecited by 198
  38. 2020Self-Supervised Learning of Audio-Visual Objects from Videocited by 186

50 most cited of 107 All 107 (JSON)

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 4; 2024: 2; 2025: 1.

8 papers.

  1. 2023Zero-shot performance of the Segment Anything Model (SAM) in 2D medical imaging: A comprehensive evaluation and practical guidelinescited by 15
  2. 2023An Empirical Analysis for Zero-Shot Multi-Label Classification on COVID-19 CT Scans and Uncurated Reportscited by 2
  3. 2024Advances in Transformers for Robotic Applications: A Reviewcited by 2
  4. 2025Hierarchical Deep Feature Fusion and Ensemble Learning for Enhanced Brain Tumor MRI Classificationcited by 2
  5. 2023A residual dense vision transformer for medical image super-resolution with segmentation-based perceptual loss fine-tuningcited by 1
  6. 2023RetSeg: Retention-based Colorectal Polyps Segmentation Networkcited by 1
  7. 2024Diffeomorphic Transformer-based Abdomen MRI-CT Deformable Image Registrationcited by 1
  8. 2022DSNet: a simple yet efficient network with dual-stream attention for lesion segmentationcited by 0