mlsanityData to 7 Oct 2026

Medical Image Segmentation with Domain Adaptation: A Survey

arXiv 2311.01702 ↗First posted 3 Nov 2023eess.IV cs.CVcited by 5

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Abstract (arXiv)

Deep learning (DL) has shown remarkable success in various medical imaging data analysis applications. However, it remains challenging for DL models to achieve good generalization, especially when the training and testing datasets are collected at sites with different scanners, due to domain shift caused by differences in data distributions. Domain adaptation has emerged as an effective means to address this challenge by mitigating domain gaps in medical imaging applications. In this review, we specifically focus on domain adaptation approaches for DL-based medical image segmentation. We first present the motivation and background knowledge underlying domain adaptations, then provide a comprehensive review of domain adaptation applications in medical image segmentations, and finally discuss the challenges, limitations, and future research trends in the field to promote the methodology development of domain adaptation in the context of medical image segmentation. Our goal was to provide researchers with up-to-date references on the applications of domain adaptation in medical image segmentation studies.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 79 of its 185 references that are in the map. First posted on arXiv 3 Nov 2023.

Builds on

79 of 185 references.

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38 more
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50 most cited of 79 All 79 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.

  1. 2025M3DA: Benchmark for Unsupervised Domain Adaptation in 3D Medical Image Segmentationcited by 1
  2. 2026DDS-UDA: Dual-Domain Synergy for Unsupervised Domain Adaptation in Joint Segmentation of Optic Disc and Optic Cupcited by 0
  3. 2026How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imagingcited by 0
  4. 2026Dual Co-Train: Cross-Dataset Ultrasound Tongue Segmentation Under Extreme Data Scarcitycited by 0
  5. 2026To do(x) or not to do(x): Medical Image Counterfactuals for Dataset Augmentationcited by 0