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Updated: Dec 11, 2025

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Advancing Medical Imaging Informatics by Deep Learning-Based Domain Adaptation.

Anirudh Choudhary1, Li Tong2, Yuanda Zhu3

  • 1Department of Computational Science and Engineering, Georgia Institute of Technology, GA, USA.

Yearbook of Medical Informatics
|August 22, 2020
PubMed
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Domain adaptation (DA) addresses deep learning (DL) data challenges in medical imaging. Unsupervised DA shows promise for segmentation tasks, offering future opportunities in transferability and multi-modal applications.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Deep learning (DL) models require large labeled datasets for medical imaging, which are challenging to acquire.
  • Multi-center datasets exhibit heterogeneity due to patient diversity and varying imaging protocols.
  • Domain adaptation (DA) is a transfer learning (TL) technique to transfer knowledge between labeled and unlabeled domains, improving model performance across datasets.

Purpose of the Study:

  • To review state-of-the-art deep learning-based domain adaptation (DA) methods in medical imaging.
  • To summarize recent advances, challenges, and opportunities in medical imaging DA.
  • To discuss promising future research directions for DA in medical imaging.

Main Methods:

  • A systematic survey of peer-reviewed publications from 2017-2020.

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  • Focus on studies reporting DA in medical imaging applications.
  • Categorization of methods by methodology, image modality, and learning scenarios.

    • Main Results:

    • Key application areas include pathology and radiology.
    • Discussed domain transformation (DT) and latent feature-space transformation (LFST) approaches.
    • Highlighted unsupervised DA's effectiveness in image segmentation and identified future development opportunities.

    Conclusions:

    • Domain adaptation (DA) is a promising solution for limited annotated medical imaging data.
    • Unsupervised DA, particularly using adversarial techniques, achieves strong performance in segmentation tasks.
    • Future opportunities lie in domain transferability, multi-modal DA, and synthetic data applications.