# Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives

arXiv: https://arxiv.org/abs/2308.01265 · HTML: https://mlsanity.com/paper/2308.01265/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 141 of its 305 references that are in the map.
- First posted on arXiv 18 Jul 2023.

## Builds on (most cited first, 30 of 141)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [CyCADA: Cycle-Consistent Adversarial Domain Adaptation](https://mlsanity.com/paper/1711.03213/) (2017)
- [ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases](https://mlsanity.com/paper/1705.02315/) (2017)
- [Deep Visual-Semantic Alignments for Generating Image Descriptions](https://mlsanity.com/paper/1412.2306/) (2014)
- [Domain Generalization: A Survey](https://mlsanity.com/paper/2103.02503/) (2021)
- [Learning to Generalize: Meta-Learning for Domain Generalization](https://mlsanity.com/paper/1710.03463/) (2017)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [Deep Visual Domain Adaptation: A Survey](https://mlsanity.com/paper/1802.03601/) (2018)
- [Kvasir-SEG: A Segmented Polyp Dataset](https://mlsanity.com/paper/1911.07069/) (2019)
- [Learning to Adapt Structured Output Space for Semantic Segmentation](https://mlsanity.com/paper/1802.10349/) (2018)
- [Continual Test-Time Domain Adaptation](https://mlsanity.com/paper/2203.13591/) (2022)
- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [Dynamic Neural Networks: A Survey](https://mlsanity.com/paper/2102.04906/) (2021)
- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- [A large annotated medical image dataset for the development and evaluation of segmentation algorithms](https://mlsanity.com/paper/1902.09063/) (2019)
- [EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos](https://mlsanity.com/paper/1602.03012/) (2016)
- [A Survey of Unsupervised Deep Domain Adaptation](https://mlsanity.com/paper/1812.02849/) (2018)
- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [Dynamic Filter Networks](https://mlsanity.com/paper/1605.09673/) (2016)
- [Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks](https://mlsanity.com/paper/1612.05424/) (2016)
- [CondConv: Conditionally Parameterized Convolutions for Efficient Inference](https://mlsanity.com/paper/1904.04971/) (2019)
- [A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions](https://mlsanity.com/paper/2006.02903/) (2020)
- [REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs](https://mlsanity.com/paper/1910.03667/) (2019)
- [A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images](https://mlsanity.com/paper/1612.00799/) (2016)

## Built on by (most cited first, 24 of 24)

- [Advancing UWF-SLO Vessel Segmentation with Source-Free Active Domain Adaptation and a Novel Multi-Center Dataset](https://mlsanity.com/paper/2406.13645/) (2024)
- [A Systematic Review of Generalization Research in Medical Image Classification](https://mlsanity.com/paper/2403.12167/) (2024)
- [Data efficient deep learning for medical image analysis: A survey](https://mlsanity.com/paper/2310.06557/) (2023)
- [Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets](https://mlsanity.com/paper/2311.01588/) (2023)
- [CACTUS: An Open Dataset and Framework for Automated Cardiac Assessment and Classification of Ultrasound Images Using Deep Transfer Learning](https://mlsanity.com/paper/2503.05604/) (2025)
- [Augmentation-based Domain Generalization and Joint Training from Multiple Source Domains for Whole Heart Segmentation](https://mlsanity.com/paper/2508.04552/) (2025)
- [AdaptDiff: Cross-Modality Domain Adaptation via Weak Conditional Semantic Diffusion for Retinal Vessel Segmentation](https://mlsanity.com/paper/2410.04648/) (2024)
- [Leveraging Modified Ex Situ Tomography Data for Segmentation of In Situ Synchrotron X-Ray Computed Tomography](https://mlsanity.com/paper/2504.19200/) (2025)
- [A3-TTA: Adaptive Anchor Alignment Test-Time Adaptation for Image Segmentation](https://mlsanity.com/paper/2602.03292/) (2026)
- [Curvature Diversity-Driven Deformation and Domain Alignment for Point Cloud](https://mlsanity.com/paper/2410.02720/) (2024)
- [Gaussian Process Emulators for Few-Shot Segmentation in Cardiac MRI](https://mlsanity.com/paper/2411.06911/) (2024)
- [Multi-Source Unsupervised Domain Adaptation with Prototype Aggregation](https://mlsanity.com/paper/2412.16255/) (2024)
- [Color-Quality Invariance for Robust Medical Image Segmentation](https://mlsanity.com/paper/2502.07200/) (2025)
- [Bridging Synthetic-to-Real Gaps: Frequency-Aware Perturbation and Selection for Single-shot Multi-Parametric Mapping Reconstruction](https://mlsanity.com/paper/2503.03475/) (2025)
- [Efficient Self-Supervised Adaptation for Medical Image Analysis](https://mlsanity.com/paper/2503.18873/) (2025)
- [Domain-Adversarial Neural Network and Explainable AI for Reducing Tissue-of-Origin Signal in Pan-cancer Mortality Classification](https://mlsanity.com/paper/2504.10343/) (2025)
- [Targeted Unlearning Using Perturbed Sign Gradient Methods With Applications On Medical Images](https://mlsanity.com/paper/2505.21872/) (2025)
- [ADAptation: Reconstruction-based Unsupervised Active Learning for Breast Ultrasound Diagnosis](https://mlsanity.com/paper/2507.00474/) (2025)
- [Unsupervised Domain Adaptation via Content Alignment for Hippocampus Segmentation](https://mlsanity.com/paper/2510.13075/) (2025)
- [Semantic-aware Random Convolution and Source Matching for Domain Generalization in Medical Image Segmentation](https://mlsanity.com/paper/2512.01510/) (2025)
- [DDS-UDA: Dual-Domain Synergy for Unsupervised Domain Adaptation in Joint Segmentation of Optic Disc and Optic Cup](https://mlsanity.com/paper/2603.13345/) (2026)
- [Improving Generalization of Deep Learning for Brain Metastases Segmentation Across Institutions](https://mlsanity.com/paper/2604.00397/) (2026)
- [A cross-process welding penetration status prediction algorithm based on unsupervised domain adaptation in laser and TIG welding](https://mlsanity.com/paper/2606.26078/) (2026)
- [Towards Practical Algorithm Selection for Unsupervised Domain Adaptation in Medical Imaging](https://mlsanity.com/paper/2607.28125/) (2026)

## Abstract (arXiv)

Deep learning has demonstrated remarkable performance across various tasks in medical imaging. However, these approaches primarily focus on supervised learning, assuming that the training and testing data are drawn from the same distribution. Unfortunately, this assumption may not always hold true in practice. To address these issues, unsupervised domain adaptation (UDA) techniques have been developed to transfer knowledge from a labeled domain to a related but unlabeled domain. In recent years, significant advancements have been made in UDA, resulting in a wide range of methodologies, including feature alignment, image translation, self-supervision, and disentangled representation methods, among others. In this paper, we provide a comprehensive literature review of recent deep UDA approaches in medical imaging from a technical perspective. Specifically, we categorize current UDA research in medical imaging into six groups and further divide them into finer subcategories based on the different tasks they perform. We also discuss the respective datasets used in the studies to assess the divergence between the different domains. Finally, we discuss emerging areas and provide insights and discussions on future research directions to conclude this survey.