# Domain Generalization for Medical Image Analysis: A Review

arXiv: https://arxiv.org/abs/2310.08598 · HTML: https://mlsanity.com/paper/2310.08598/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- Builds on 98 of its 178 references that are in the map.
- First posted on arXiv 5 Oct 2023.

## Builds on (most cited first, 30 of 98)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges](https://mlsanity.com/paper/2104.13478/) (2021)
- [StarGAN v2: Diverse Image Synthesis for Multiple Domains](https://mlsanity.com/paper/1912.01865/) (2019)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks](https://mlsanity.com/paper/1710.11063/) (2017)
- [Generalized Out-of-Distribution Detection: A Survey](https://mlsanity.com/paper/2110.11334/) (2021)
- [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021)
- [MINE: Mutual Information Neural Estimation](https://mlsanity.com/paper/1801.04062/) (2018)
- [Towards Out-Of-Distribution Generalization: A Survey](https://mlsanity.com/paper/2108.13624/) (2021)
- [Domain Generalization via Model-Agnostic Learning of Semantic Features](https://mlsanity.com/paper/1910.13580/) (2019)
- [Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning](https://mlsanity.com/paper/2206.02647/) (2022)
- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [A Fourier-based Framework for Domain Generalization](https://mlsanity.com/paper/2105.11120/) (2021)
- [FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space](https://mlsanity.com/paper/2103.06030/) (2021)
- [SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining](https://mlsanity.com/paper/2107.09559/) (2021)
- [Domain Generalization using Causal Matching](https://mlsanity.com/paper/2006.07500/) (2020)
- [FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare](https://mlsanity.com/paper/1907.09173/) (2019)
- [Privacy-preserving Federated Brain Tumour Segmentation](https://mlsanity.com/paper/1910.00962/) (2019)
- [Explainable artificial intelligence (XAI) in deep learning-based medical image analysis](https://mlsanity.com/paper/2107.10912/) (2021)
- [Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation](https://mlsanity.com/paper/2007.09886/) (2020)
- [Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains](https://mlsanity.com/paper/2007.02035/) (2020)
- [Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization](https://mlsanity.com/paper/2009.12829/) (2020)
- [DIVA: Domain Invariant Variational Autoencoders](https://mlsanity.com/paper/1905.10427/) (2019)
- [Explainable deep learning models in medical image analysis](https://mlsanity.com/paper/2005.13799/) (2020)
- [Test-Time Adaptable Neural Networks for Robust Medical Image Segmentation](https://mlsanity.com/paper/2004.04668/) (2020)

## Built on by (most cited first, 30 of 48)

- [Towards Generalisable Foundation Models for Brain MRI](https://mlsanity.com/paper/2510.23415/) (2025)
- [The Perils of Optimizing Learned Reward Functions: Low Training Error Does Not Guarantee Low Regret](https://mlsanity.com/paper/2406.15753/) (2024)
- [RobustEMD: Domain Robust Matching for Cross-domain Few-shot Medical Image Segmentation](https://mlsanity.com/paper/2410.01110/) (2024)
- [Modality-Balanced Collaborative Distillation for Multi-Modal Domain Generalization](https://mlsanity.com/paper/2511.20258/) (2025)
- [Medical Knowledge Intervention Prompt Tuning for Medical Image Classification](https://mlsanity.com/paper/2511.12639/) (2025)
- [Deep Generative Classification of Blood Cell Morphology](https://mlsanity.com/paper/2408.08982/) (2024)
- [BrainDINO: A Brain MRI Foundation Model for Generalizable Clinical Representation Learning](https://mlsanity.com/paper/2604.27277/) (2026)
- [Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift](https://mlsanity.com/paper/2509.09397/) (2025)
- [FIESTA: Fourier-Based Semantic Augmentation with Uncertainty Guidance for Enhanced Domain Generalizability in Medical Image Segmentation](https://mlsanity.com/paper/2406.14308/) (2024)
- [Can Diffusion Models Bridge the Domain Gap in Cardiac MR Imaging?](https://mlsanity.com/paper/2508.06327/) (2025)
- [In-hoc Concept Representations to Regularise Deep Learning in Medical Imaging](https://mlsanity.com/paper/2508.13880/) (2025)
- [Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning](https://mlsanity.com/paper/2509.00745/) (2025)
- [GNN-based Unified Deep Learning](https://mlsanity.com/paper/2508.10583/) (2025)
- [Stylizing ViT: Anatomy-Preserving Instance Style Transfer for Domain Generalization](https://mlsanity.com/paper/2601.17586/) (2026)
- [Managing Diabetic Retinopathy with Deep Learning: A Data Centric Overview](https://mlsanity.com/paper/2604.02448/) (2026)
- [Parameter Efficient Fine-tuning for Domain-specific Gastrointestinal Disease Recognition](https://mlsanity.com/paper/2604.10451/) (2026)
- [Beyond Random Partitioning: Unsupervised Spatio-Temporal Stratification for Cohort Balancing in Longitudinal Medical Imaging](https://mlsanity.com/paper/2608.00073/) (2026)
- [Color-Quality Invariance for Robust Medical Image Segmentation](https://mlsanity.com/paper/2502.07200/) (2025)
- [Multi-Modal Artificial Intelligence of Embryo Grading and Pregnancy Prediction in Assisted Reproductive Technology: A Review](https://mlsanity.com/paper/2505.20306/) (2025)
- [Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation](https://mlsanity.com/paper/2507.23326/) (2025)
- [Disentanglement of Biological and Technical Factors via Latent Space Rotation in Clinical Imaging Improves Disease Pattern Discovery](https://mlsanity.com/paper/2509.11436/) (2025)
- [MRN: Harnessing 2D Vision Foundation Models for Diagnosing Parkinson's Disease with Limited 3D MR Data](https://mlsanity.com/paper/2509.17566/) (2025)
- [FedDAPL: Toward Client-Private Generalization in Federated Learning](https://mlsanity.com/paper/2509.23688/) (2025)
- [TreeFedDG: Alleviating Global Drift in Federated Domain Generalization for Medical Image Segmentation](https://mlsanity.com/paper/2510.18268/) (2025)
- [Uncertainty-Guided Selective Adaptation Enables Cross-Platform Predictive Fluorescence Microscopy](https://mlsanity.com/paper/2511.12006/) (2025)
- [MMRINet: Efficient Mamba-Based Segmentation with Dual-Path Refinement for Low-Resource MRI Analysis](https://mlsanity.com/paper/2511.12193/) (2025)
- [XAI-Driven Diagnosis of Generalization Failure in State-Space Cerebrovascular Segmentation Models: A Case Study on Domain Shift Between RSNA and TopCoW Datasets](https://mlsanity.com/paper/2512.13977/) (2025)
- [A-QCF-Net: An Adaptive Quaternion Cross-Fusion Network for Multimodal Liver Tumor Segmentation from Unpaired Datasets](https://mlsanity.com/paper/2512.21760/) (2025)
- [Controllable Diffusion-Based Lesion Inpainting for Scalable Histopathology Data Augmentation](https://mlsanity.com/paper/2601.08127/) (2026)
- [SegRap2025: A Benchmark of Gross Tumor Volume and Lymph Node Clinical Target Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma](https://mlsanity.com/paper/2601.20575/) (2026)

## Abstract (arXiv)

Medical image analysis (MedIA) has become an essential tool in medicine and healthcare, aiding in disease diagnosis, prognosis, and treatment planning, and recent successes in deep learning (DL) have made significant contributions to its advances. However, deploying DL models for MedIA in real-world situations remains challenging due to their failure to generalize across the distributional gap between training and testing samples - a problem known as domain shift. Researchers have dedicated their efforts to developing various DL methods to adapt and perform robustly on unknown and out-of-distribution (OOD) data distributions. This article comprehensively reviews domain generalization (DG) studies specifically tailored for MedIA. We provide a holistic view of how DG techniques interact within the broader MedIA system, going beyond methodologies to consider the operational implications on the entire MedIA workflow. Specifically, we categorize DG methods into data-level, feature-level, model-level, and analysis-level methods. We show how those methods can be used in various stages of the MedIA workflow with DL equipped from data acquisition to model prediction and analysis. Furthermore, we critically analyze the strengths and weaknesses of various methods, unveiling future research opportunities.