mlsanityData to 7 Oct 2026

Transformers in Medical Image Analysis: A Review

arXiv 2202.12165 ↗First posted 24 Feb 2022cs.CVcited by 41

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Abstract (arXiv)

Transformers have dominated the field of natural language processing, and recently impacted the computer vision area. In the field of medical image analysis, Transformers have also been successfully applied to full-stack clinical applications, including image synthesis/reconstruction, registration, segmentation, detection, and diagnosis. Our paper aims to promote awareness and application of Transformers in the field of medical image analysis. Specifically, we first overview the core concepts of the attention mechanism built into Transformers and other basic components. Second, we review various Transformer architectures tailored for medical image applications and discuss their limitations. Within this review, we investigate key challenges revolving around the use of Transformers in different learning paradigms, improving the model efficiency, and their coupling with other techniques. We hope this review can give a comprehensive picture of Transformers to the readers in the field of medical image analysis.

Key facts. Cited by 41 papers in the map (data through 7 Oct 2026). Builds on 114 of its 220 references that are in the map. First posted on arXiv 24 Feb 2022.

Builds on

114 of 220 references.

References · 114

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38 more
  1. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,552
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 9; 2023: 14; 2024: 11; 2025: 6; 2026: 1.

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  1. 2023Conditional Diffusion Models for Semantic 3D Brain MRI Synthesiscited by 85
  2. 2023Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Reviewcited by 62
  3. 2022MALUNet: A Multi-Attention and Light-weight UNet for Skin Lesion Segmentationcited by 59
  4. 2022A Survey on Deep Learning for Skin Lesion Segmentationcited by 22
  5. 2023Transformers in Healthcare: A Surveycited by 17
  6. 2022Probabilistic Integration of Object Level Annotations in Chest X-ray Classificationcited by 10
  7. 2022The Lighter The Better: Rethinking Transformers in Medical Image Segmentation Through Adaptive Pruningcited by 9
  8. 2022Recent Progress in Transformer-based Medical Image Analysiscited by 9
  9. 2023Attention Mechanisms in Medical Image Segmentation: A Surveycited by 9
  10. 2023Which Transformer to Favor: A Comparative Analysis of Efficiency in Vision Transformerscited by 9
  11. 2023Fully transformer-based biomarker prediction from colorectal cancer histology: a large-scale multicentric studycited by 6
  12. 2025Boosting multi-demographic federated learning for chest radiograph analysis using general-purpose self-supervised representationscited by 6
29 more
  1. 2025Scaling Up Biomedical Vision-Language Models: Fine-Tuning, Instruction Tuning, and Multi-Modal Learningcited by 6
  2. 2024Transformers-based architectures for stroke segmentation: A reviewcited by 5
  3. 2023Artificial Intelligence in Ovarian Cancer Histopathology: A Systematic Reviewcited by 4
  4. 2023A Survey on Image-text Multimodal Modelscited by 4
  5. 2024Gradient Boosting Decision Trees on Medical Diagnosis over Tabular Datacited by 4
  6. 2025A Self-supervised Multimodal Deep Learning Approach to Differentiate Post-radiotherapy Progression from Pseudoprogression in Glioblastomacited by 4
  7. 2025Robust Fairness Vision-Language Learning for Medical Image Analysiscited by 4
  8. 2024Federated and Transfer Learning for Cancer Detection Based on Image Analysiscited by 3
  9. 2024Lesion-Aware Cross-Phase Attention Network for Renal Tumor Subtype Classification on Multi-Phase CT Scanscited by 3
  10. 2024Impact of imperfect annotations on CNN training and performance for instance segmentation and classification in digital pathologycited by 3
  11. 2022Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligencecited by 2
  12. 2022MultiCrossViT: Multimodal Vision Transformer for Schizophrenia Prediction using Structural MRI and Functional Network Connectivity Datacited by 2
  13. 2023Transformer-based end-to-end classification of variable-length volumetric datacited by 2
  14. 2023Improving diagnosis and prognosis of lung cancer using vision transformers: A scoping reviewcited by 2
  15. 2023Automatic hip osteoarthritis grading with uncertainty estimation from computed tomography using digitally-reconstructed radiographscited by 2
  16. 2024Advances in Transformers for Robotic Applications: A Reviewcited by 2
  17. 2022SQ-Swin: a Pretrained Siamese Quadratic Swin Transformer for Lettuce Browning Predictioncited by 1
  18. 2023A residual dense vision transformer for medical image super-resolution with segmentation-based perceptual loss fine-tuningcited by 1
  19. 2024Efficient Feature Extraction Using Light-Weight CNN Attention-Based Deep Learning Architectures for Ultrasound Fetal Plane Classificationcited by 1
  20. 2024Advancing Histopathology with Deep Learning Under Data Scarcity: A Decade in Reviewcited by 1
  21. 2025EPEE: Towards Efficient and Effective Foundation Models in Biomedicinecited by 1
  22. 2022OMSN and FAROS: OCTA Microstructure Segmentation Network and Fully Annotated Retinal OCTA Segmentation Datasetcited by 0
  23. 2023DPSeq: A Novel and Efficient Digital Pathology Classifier for Predicting Cancer Biomarkers using Sequencer Architecturecited by 0
  24. 2023Identification of Abnormality in Maize Plants From UAV Images Using Deep Learning Approachescited by 0
  25. 2024A Survey on Cell Nuclei Instance Segmentation and Classification: Leveraging Context and Attentioncited by 0
  26. 2024HistoSPACE: Histology-Inspired Spatial Transcriptome Prediction And Characterization Enginecited by 0
  27. 2024COMFORT: A Continual Fine-Tuning Framework for Foundation Models Targeted at Consumer Healthcarecited by 0
  28. 2025Multi-Plane Vision Transformer for Hemorrhage Classification Using Axial and Sagittal MRI Datacited by 0
  29. 2026Performance of Small Language Model Pretraining on FABRIC: An Empirical Studycited by 0