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Domain Adaptation for Medical Image Analysis: A Survey

arXiv 2102.09508 ↗First posted 18 Feb 2021cs.CV cs.LG eess.IVcited by 351

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Abstract (arXiv)

Machine learning techniques used in computer-aided medical image analysis usually suffer from the domain shift problem caused by different distributions between source/reference data and target data. As a promising solution, domain adaptation has attracted considerable attention in recent years. The aim of this paper is to survey the recent advances of domain adaptation methods in medical image analysis. We first present the motivation of introducing domain adaptation techniques to tackle domain heterogeneity issues for medical image analysis. Then we provide a review of recent domain adaptation models in various medical image analysis tasks. We categorize the existing methods into shallow and deep models, and each of them is further divided into supervised, semi-supervised and unsupervised methods. We also provide a brief summary of the benchmark medical image datasets that support current domain adaptation research. This survey will enable researchers to gain a better understanding of the current status, challenges.

Key facts. Cited by 351 papers in the map (data through 7 Oct 2026). Builds on 71 of its 180 references that are in the map. First posted on arXiv 18 Feb 2021.

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71 of 180 references.

References · 71

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38 more
  1. 2020Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptationcited by 844
  2. 2018Deep Visual Domain Adaptation: A Surveycited by 634
  3. 2018Exploring the Limits of Weakly Supervised Pretrainingcited by 604
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  17. 2020Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentationcited by 134
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  19. 2019Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentationcited by 121
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  22. 2018Unsupervised domain adaptation for medical imaging segmentation with self-ensemblingcited by 94
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  25. 2019Multi-source Distilling Domain Adaptationcited by 73
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50 most cited of 71 All 71 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 10; 2022: 49; 2023: 60; 2024: 65; 2025: 92; 2026: 75.

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  1. 2022Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistencycited by 246
  2. 2023Data-centric Artificial Intelligence: A Surveycited by 202
  3. 2022Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectivescited by 98
  4. 2022Source-Free Unsupervised Domain Adaptation: A Surveycited by 79
  5. 2023Federated Learning for Medical Image Analysis: A Surveycited by 77
  6. 2024Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directionscited by 77
  7. 2022CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentationcited by 58
  8. 2022ImageCAS: A Large-Scale Dataset and Benchmark for Coronary Artery Segmentation based on Computed Tomography Angiography Imagescited by 54
  9. 2022Optimally tackling covariate shift in RKHS-based nonparametric regressioncited by 53
  10. 2023Domain Generalization for Medical Image Analysis: A Reviewcited by 47
  11. 2022SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networkscited by 36
  12. 2024Dual Prototype Evolving for Test-Time Generalization of Vision-Language Modelscited by 36
38 more
  1. 2022On-the-Fly Test-time Adaptation for Medical Image Segmentationcited by 34
  2. 2022Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectivescited by 32
  3. 2023A comprehensive survey on deep active learning in medical image analysiscited by 32
  4. 2023Devil is in Channels: Contrastive Single Domain Generalization for Medical Image Segmentationcited by 30
  5. 2023Domain Generalization in Computational Pathology: Survey and Guidelinescited by 29
  6. 2022LE-UDA: Label-efficient unsupervised domain adaptation for medical image segmentationcited by 27
  7. 2023DeSAM: Decoupled Segment Anything Model for Generalizable Medical Image Segmentationcited by 26
  8. 2023Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectivescited by 24
  9. 2026Medical SAM3: A Foundation Model for Universal Prompt-Driven Medical Image Segmentationcited by 23
  10. 2023Continual Learning in Medical Image Analysis: A Comprehensive Review of Recent Advancements and Future Prospectscited by 22
  11. 2023FVP: Fourier Visual Prompting for Source-Free Unsupervised Domain Adaptation of Medical Image Segmentationcited by 21
  12. 2023UPL-SFDA: Uncertainty-aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentationcited by 21
  13. 2022Deep Learning for Computational Cytology: A Surveycited by 20
  14. 2024Has Multimodal Learning Delivered Universal Intelligence in Healthcare? A Comprehensive Surveycited by 20
  15. 2025MedVAE: Efficient Automated Interpretation of Medical Images with Large-Scale Generalizable Autoencoderscited by 20
  16. 2022Divide to Adapt: Mitigating Confirmation Bias for Domain Adaptation of Black-Box Predictorscited by 19
  17. 2024FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classificationcited by 19
  18. 2024Fr'echet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasetscited by 19
  19. 2025How Well Do Supervised 3D Models Transfer to Medical Imaging Tasks?cited by 19
  20. 2021Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classificationcited by 18
  21. 2022ProSFDA: Prompt Learning based Source-free Domain Adaptation for Medical Image Segmentationcited by 18
  22. 2022CDDSA: Contrastive Domain Disentanglement and Style Augmentation for Generalizable Medical Image Segmentationcited by 18
  23. 2025CheXWorld: Exploring Image World Modeling for Radiograph Representation Learningcited by 18
  24. 2023Stethoscope-guided Supervised Contrastive Learning for Cross-domain Adaptation on Respiratory Sound Classificationcited by 16
  25. 2024From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcarecited by 16
  26. 2024The Impact of Scanner Domain Shift on Deep Learning Performance in Medical Imaging: an Experimental Studycited by 15
  27. 2022On the in vivo recognition of kidney stones using machine learningcited by 14
  28. 2022PyMIC: A deep learning toolkit for annotation-efficient medical image segmentationcited by 14
  29. 2023Can We Evaluate Domain Adaptation Models Without Target-Domain Labels?cited by 14
  30. 2024Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image Segmentationcited by 14
  31. 2024A Textbook Remedy for Domain Shifts: Knowledge Priors for Medical Image Analysiscited by 14
  32. 2023Energy-Guided Continuous Entropic Barycenter Estimation for General Costscited by 12
  33. 2024Align and Distill: Unifying and Improving Domain Adaptive Object Detectioncited by 12
  34. 2021DICOM Imaging Router: An Open Deep Learning Framework for Classification of Body Parts from DICOM X-ray Scanscited by 11
  35. 2022CellTranspose: Few-shot Domain Adaptation for Cellular Instance Segmentationcited by 11
  36. 2023Order-preserving Consistency Regularization for Domain Adaptation and Generalizationcited by 11
  37. 2024Advancing UWF-SLO Vessel Segmentation with Source-Free Active Domain Adaptation and a Novel Multi-Center Datasetcited by 11
  38. 2021Multiple Sclerosis Lesion Analysis in Brain Magnetic Resonance Images: Techniques and Clinical Applicationscited by 10

50 most cited of 351 All 351 (JSON)