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Cited by 1,057Domain Adaptation and Few-Shot LearningMedical Image Segmentation TechniquesArtificial Intelligence in Healthcare and Education

Domain adaptation for medical image analysis: A survey

Open access
Guan, Hao, Liu, Mingxia·2022·IEEE Transactions on Biomedical Engineering
DOI 10.1109/tbme.2021.3117407PMC PMC9011180
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Abstract

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 and future directions of this energetic research field.

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  • Domain adaptation is a sound machine learning technique that has been widely used in medical image analysis.

    “Domain adaptation is a sound machine learning technique that has been widely used in medical image analysis [3].”

    From Federated learning for medical image analysis: A survey (2024) · cites this paper as [3]

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Research works printed but not linked: 10

  1. [33]Raghu M, “ Transfusion: Understanding transfer learning for medical imaging,” in Proc. Adv. Neural Inf. Process. Syst., 2019, pp. 3347– 3357.
  2. [61]Goodfellow I, Deep Learning. Cambridge, MA, USA: MIT Press, 2016, pp. 1– 802.
  3. [100]Panfilov E, “ Improving robustness of deep learning based knee MRI segmentation: Mixup and adversarial domain adaptation,” in Proc. IEEE Int. Conf. Comput. Vis. Workshops, 2019, pp. 1– 10.
  4. [115]Goodfellow I, “ Generative adversarial nets,” in Proc. Adv. Neural Inf. Process. Syst., 2014, pp. 1– 9.
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Authors
Guan, H., & Liu, M.
Affiliations
University of North Carolina at Chapel Hill (US)
ORCID
Venue
IEEE Transactions on Biomedical Engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2022-03
Volume
69
Issue
3
Pages
1173-1185
ISSN
  • 0018-9294 (ISSN-L)
  • 1558-2531
DOI
10.1109/tbme.2021.3117407
PMC
PMC9011180
OpenAlex
W3130794141
Cited by
1,057
References
149
Citations by year
2026: 287 · 2025: 277 · 2024: 227 · 2023: 168 · 2022: 81 · 2021: 16 · 2020: 1
FWCI
8.89
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Transactions on Biomedical Engineering (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University) (cc-by)
Funders
NIH (AG041721)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
domain adaptation, medical image analysis, domain shift, domain heterogeneity, computer-aided diagnosis, semi-supervised learning, unsupervised learning, medical image dataset
MeSH terms
Machine Learning, Image Processing, Computer-Assisted, Benchmarking

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