ReviewIEEE transactions on bio-medical engineering2022
Domain Adaptation for Medical Image Analysis: A Survey.
Review in IEEE transactions on bio-medical engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 235 papers, 3 of them syntheses that pooled it.
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Who cites it
235 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Deep learning in ultrasound tongue imaging: a systematic review toward automated detection of speech sound disorders.Frontiers in artificial intelligence · 2025Pooled it
- Source-free unsupervised domain adaptation: A survey.Neural networks : the official journal of the International Neural Network Society · 2024Pooled it
- A Systematic Review and Meta-Analysis of Applying Deep Learning in the Prediction of the Risk of Cardiovascular Diseases From Retinal Images.Translational vision science & technology · 2023Pooled it
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- External evaluation of a deep learning-based algorithm for automated lung tumor and esophagus segmentation on CBCT.Biomedical physics & engineering express · 2026Article
- Parameter-Efficient LoRA-GRL Adaptation for Cross-Center Classification of Benign and Malignant Lung Nodules on Heterogeneous Standard-Dose Chest CT: A Multi-Institutional Study from Palestine.Journal of imaging · 2026Article
- Synthetic-to-Clinical Ensemble Learning for Volumetric Breast Tumor Segmentation in Digital Breast Tomosynthesis Under Limited Annotated Data.Diagnostics (Basel, Switzerland) · 2026Article
- Article
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- Keeping Medical AI Healthy and Trustworthy: A Review of Detection and Correction Methods for System Degradation.IEEE transactions on bio-medical engineering · 2026Review
- Graph identification of proteins in tomograms (GRIP-Tomo) 2.0: Topologically aware classification for proteins.Protein science : a publication of the Protein Society · 2026Article
- Deep learning for developmental dysplasia of the hip: comparative classification strategies for ultrasound imaging with heterogeneous image quality.Physical and engineering sciences in medicine · 2026Article
- Cross-Domain Generalization of Deep Learning Architectures for Cephalometric Landmark Detection: A Dual-Dataset and Multi-Device Benchmark.Diagnostics (Basel, Switzerland) · 2026Article
- Benchmarking Deep Learning for NSCLC PET/CT Segmentation on a Histologically Confirmed Vietnamese Dataset: Validation and Generalization.Journal of imaging · 2026Article
- Using Latent Representations to Link Disjoint Longitudinal Data for Mixed-Effects Regression.Statistics in medicine · 2026Article
- Deep Learning Based Cross-Modality Histological Brain Section Registration in Multiple Species Using Synthetic Images.bioRxiv : the preprint server for biology · 2026Article
- AGTS-Net: Anatomically Guided Two-Stage Network for Actionable Stroke Classification in Complete NCCT Studies.Bioengineering (Basel, Switzerland) · 2026Article
- A Systematic Review and Meta-analysis of Two-Dimensional Breast Ultrasound Radiomics: Implications for Lesion Characterization and Diagnostic Accuracy.Journal of imaging informatics in medicine · 2026Review
- Benchmarking YOLOs in breast ultrasound lesion segmentation.Quantitative imaging in medicine and surgery · 2026Article
- Validating predictive models on EHRs data : lessons learnt from the NCDR-IMPACT-score applied on a French cardiac catheterization cohort.BMC cardiovascular disorders · 2026Article
175 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.