mlsanityData to 14 Oct 2026

Domain Adaptation and Generalization on Functional Medical Images: A Systematic Survey

arXiv 2212.03176 ↗First posted 4 Dec 2022eess.IV cs.CV cs.LGcited by 4

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Abstract (arXiv)

Machine learning algorithms have revolutionized different fields, including natural language processing, computer vision, signal processing, and medical data processing. Despite the excellent capabilities of machine learning algorithms in various tasks and areas, the performance of these models mainly deteriorates when there is a shift in the test and training data distributions. This gap occurs due to the violation of the fundamental assumption that the training and test data are independent and identically distributed (i.i.d). In real-world scenarios where collecting data from all possible domains for training is costly and even impossible, the i.i.d assumption can hardly be satisfied. The problem is even more severe in the case of medical images and signals because it requires either expensive equipment or a meticulous experimentation setup to collect data, even for a single domain. Additionally, the decrease in performance may have severe consequences in the analysis of medical records. As a result of such problems, the ability to generalize and adapt under distribution shifts (domain generalization (DG) and domain adaptation (DA)) is essential for the analysis of medical data. This paper provides the first systematic review of DG and DA on functional brain signals to fill the gap of the absence of a comprehensive study in this era. We provide detailed explanations and categorizations of datasets, approaches, and architectures used in DG and DA on functional brain images. We further address the attention-worthy future tracks in this field.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 59 of its 275 references that are in the map. First posted on arXiv 4 Dec 2022.

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59 of 275 references.

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38 more
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50 most cited of 59 All 59 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 0; 2025: 0; 2026: 1.

4 papers.

  1. 2023Domain Generalization for Medical Image Analysis: A Reviewcited by 48
  2. 2023Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectivescited by 24
  3. 2023SSVEP-DAN: A Data Alignment Network for SSVEP-based Brain Computer Interfacescited by 1
  4. 2026How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imagingcited by 0