Domain Adaptation for Medical Image Analysis: A Survey

Overview

Journal IEEE Trans Biomed Eng

Specialties Biomedical Engineering
Biophysics

Date 2021 Oct 4

PMID 34606445

Citations 94

Authors

Hao Guan

Mingxia Liu

Affiliations

Soon will be listed here.

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.

Citing Articles

Hybrid multi-modality multi-task learning for forecasting progression trajectories in subjective cognitive decline.

Yu M, Fang Y, Liu Y, Bozoki A, Xiao S, Yue L Neural Netw. 2025; 186:107263.

PMID: 39985974 PMC: 11893250. DOI: 10.1016/j.neunet.2025.107263.

MRI-assessed Dynamic Hyperinflation Induced by Tachypnea in Chronic Obstructive Pulmonary Disease: The SPIROMICS-HF Study.

Zhang X, Cooper C, Prince M, Ambale-Venkatesh B, Agarwal P, Backman M Radiol Cardiothorac Imaging. 2025; 7(1):e240053.

PMID: 39836055 PMC: 11880862. DOI: 10.1148/ryct.240053.

Issues and Limitations on the Road to Fair and Inclusive AI Solutions for Biomedical Challenges.

Faust O, Salvi M, Barua P, Chakraborty S, Molinari F, Rajendra Acharya U Sensors (Basel). 2025; 25(1.

PMID: 39796996 PMC: 11723364. DOI: 10.3390/s25010205.

Robust Automated Mouse Micro-CT Segmentation Using Swin UNEt TRansformers.

Jiang L, Xu D, Xu Q, Chatziioannou A, Iwamoto K, Hui S Bioengineering (Basel). 2025; 11(12.

PMID: 39768073 PMC: 11673508. DOI: 10.3390/bioengineering11121255.

Source-free domain transfer algorithm with reduced style sensitivity for medical image segmentation.

Lin J, Yu X, Wang Z, Ma C PLoS One. 2024; 19(12):e0309118.

PMID: 39729484 PMC: 11676784. DOI: 10.1371/journal.pone.0309118.

References

Shen D, Wu G, Suk H . Deep Learning in Medical Image Analysis. Annu Rev Biomed Eng. 2017; 19:221-248. PMC: 5479722. DOI: 10.1146/annurev-bioeng-071516-044442. View

Bermudez-Chacon R, Altingovde O, Becker C, Salzmann M, Fua P . Visual Correspondences for Unsupervised Domain Adaptation on Electron Microscopy Images. IEEE Trans Med Imaging. 2019; 39(4):1256-1267. DOI: 10.1109/TMI.2019.2946462. View

Van Essen D, Smith S, Barch D, Behrens T, Yacoub E, Ugurbil K . The WU-Minn Human Connectome Project: an overview. Neuroimage. 2013; 80:62-79. PMC: 3724347. DOI: 10.1016/j.neuroimage.2013.05.041. View

Menze B, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J . The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Trans Med Imaging. 2014; 34(10):1993-2024. PMC: 4833122. DOI: 10.1109/TMI.2014.2377694. View

Mazurowski M, Buda M, Saha A, Bashir M . Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI. J Magn Reson Imaging. 2018; 49(4):939-954. PMC: 6483404. DOI: 10.1002/jmri.26534. View

Zhang L, Wang X, Yang D, Sanford T, Harmon S, Turkbey B . Generalizing Deep Learning for Medical Image Segmentation to Unseen Domains via Deep Stacked Transformation. IEEE Trans Med Imaging. 2020; 39(7):2531-2540. PMC: 7393676. DOI: 10.1109/TMI.2020.2973595. View

Huang Y, Zheng H, Liu C, Ding X, Rohde G . Epithelium-Stroma Classification via Convolutional Neural Networks and Unsupervised Domain Adaptation in Histopathological Images. IEEE J Biomed Health Inform. 2017; 21(6):1625-1632. DOI: 10.1109/JBHI.2017.2691738. View

Zhang L, Gao X . Transfer Adaptation Learning: A Decade Survey. IEEE Trans Neural Netw Learn Syst. 2022; PP. DOI: 10.1109/TNNLS.2022.3183326. View

Hoover A, Kouznetsova V, Goldbaum M . Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans Med Imaging. 2000; 19(3):203-10. DOI: 10.1109/42.845178. View

10.

Wilson G, Cook D . A Survey of Unsupervised Deep Domain Adaptation. ACM Trans Intell Syst Technol. 2021; 11(5):1-46. PMC: 8323662. DOI: 10.1145/3400066. View

11.

Xing F, Bennett T, Ghosh D . Adversarial Domain Adaptation and Pseudo-Labeling for Cross-Modality Microscopy Image Quantification. Med Image Comput Comput Assist Interv. 2019; 11764:740-749. PMC: 6903918. DOI: 10.1007/978-3-030-32239-7_82. View

12.

Becker C, Christoudias C, Fua P . Domain adaptation for microscopy imaging. IEEE Trans Med Imaging. 2014; 34(5):1125-39. DOI: 10.1109/TMI.2014.2376872. View

13.

Ren S, He K, Girshick R, Sun J . Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Trans Pattern Anal Mach Intell. 2016; 39(6):1137-1149. DOI: 10.1109/TPAMI.2016.2577031. View

14.

Demner-Fushman D, Kohli M, Rosenman M, Shooshan S, Rodriguez L, Antani S . Preparing a collection of radiology examinations for distribution and retrieval. J Am Med Inform Assoc. 2015; 23(2):304-10. PMC: 5009925. DOI: 10.1093/jamia/ocv080. View

15.

Mahmood F, Chen R, Durr N . Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training. IEEE Trans Med Imaging. 2018; 37(12):2572-2581. DOI: 10.1109/TMI.2018.2842767. View

16.

Valverde S, Salem M, Cabezas M, Pareto D, Vilanova J, Ramio-Torrenta L . One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks. Neuroimage Clin. 2018; 21:101638. PMC: 6413299. DOI: 10.1016/j.nicl.2018.101638. View

17.

Bron E, Smits M, van der Flier W, Vrenken H, Barkhof F, Scheltens P . Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge. Neuroimage. 2015; 111:562-79. PMC: 4943029. DOI: 10.1016/j.neuroimage.2015.01.048. View

18.

AlBadawy E, Saha A, Mazurowski M . Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing. Med Phys. 2018; 45(3):1150-1158. DOI: 10.1002/mp.12752. View

19.

Staal J, Abramoff M, Niemeijer M, Viergever M, van Ginneken B . Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imaging. 2004; 23(4):501-9. DOI: 10.1109/TMI.2004.825627. View

20.

Wang S, Yu L, Yang X, Fu C, Heng P . Patch-Based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation. IEEE Trans Med Imaging. 2019; 38(11):2485-2495. DOI: 10.1109/TMI.2019.2899910. View