# Frequency-mixed Single-source Domain Generalization for Medical Image Segmentation

arXiv: https://arxiv.org/abs/2307.09005 · HTML: https://mlsanity.com/paper/2307.09005/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 14 references that are in the map.
- First posted on arXiv 18 Jul 2023.

## Builds on (most cited first, 11 of 11)

- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Domain Generalization: A Survey](https://mlsanity.com/paper/2103.02503/) (2021)
- [FDA: Fourier Domain Adaptation for Semantic Segmentation](https://mlsanity.com/paper/2004.05498/) (2020)
- [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)
- [CE-Net: Context Encoder Network for 2D Medical Image Segmentation](https://mlsanity.com/paper/1903.02740/) (2019)
- [Causality-inspired Single-source Domain Generalization for Medical Image Segmentation](https://mlsanity.com/paper/2111.12525/) (2021)
- [Semantic-Aware Domain Generalized Segmentation](https://mlsanity.com/paper/2204.00822/) (2022)
- [An Annotation-free Restoration Network for Cataractous Fundus Images](https://mlsanity.com/paper/2203.07737/) (2022)
- [Out-of-Domain Generalization from a Single Source: An Uncertainty Quantification Approach](https://mlsanity.com/paper/2108.02888/) (2021)
- [Structure-consistent Restoration Network for Cataract Fundus Image Enhancement](https://mlsanity.com/paper/2206.04684/) (2022)

## Built on by (most cited first, 21 of 21)

- [Domain Generalization for Medical Image Analysis: A Review](https://mlsanity.com/paper/2310.08598/) (2023)
- [A Generic Fundus Image Enhancement Network Boosted by Frequency Self-supervised Representation Learning](https://mlsanity.com/paper/2309.00885/) (2023)
- [Enhancing and Adapting in the Clinic: Source-free Unsupervised Domain Adaptation for Medical Image Enhancement](https://mlsanity.com/paper/2312.01338/) (2023)
- [A Survey on Domain Generalization for Medical Image Analysis](https://mlsanity.com/paper/2402.05035/) (2024)
- [MoreStyle: Relax Low-frequency Constraint of Fourier-based Image Reconstruction in Generalizable Medical Image Segmentation](https://mlsanity.com/paper/2403.11689/) (2024)
- [Prompting Segment Anything Model with Domain-Adaptive Prototype for Generalizable Medical Image Segmentation](https://mlsanity.com/paper/2409.12522/) (2024)
- [Improving cross-domain brain tissue segmentation in fetal MRI with synthetic data](https://mlsanity.com/paper/2403.15103/) (2024)
- [Medical Image Segmentation via Single-Source Domain Generalization with Random Amplitude Spectrum Synthesis](https://mlsanity.com/paper/2409.04768/) (2024)
- [CoSAM: Self-Correcting SAM for Domain Generalization in 2D Medical Image Segmentation](https://mlsanity.com/paper/2411.10136/) (2024)
- [RaffeSDG: Random Frequency Filtering enabled Single-source Domain Generalization for Medical Image Segmentation](https://mlsanity.com/paper/2405.01228/) (2024)
- [Evaluating Synthetic Data Generation for Domain Generalization in Fetal Brain MRI Segmentation](https://mlsanity.com/paper/2411.06842/) (2024)
- [FIESTA: Fourier-Based Semantic Augmentation with Uncertainty Guidance for Enhanced Domain Generalizability in Medical Image Segmentation](https://mlsanity.com/paper/2406.14308/) (2024)
- [Style Content Decomposition-based Data Augmentation for Domain Generalizable Medical Image Segmentation](https://mlsanity.com/paper/2502.20619/) (2025)
- [Fully Automated SAM for Single-source Domain Generalization in Medical Image Segmentation](https://mlsanity.com/paper/2507.17281/) (2025)
- [AngioDG: Interpretable Channel-informed Feature-modulated Single-source Domain Generalization for Coronary Vessel Segmentation in X-ray Angiography](https://mlsanity.com/paper/2511.17724/) (2025)
- [Higher-Order Domain Generalization in Magnetic Resonance-Based Assessment of Alzheimer's Disease](https://mlsanity.com/paper/2601.01485/) (2026)
- [Adaptive Affinity-Based Generalization For MRI Imaging Segmentation Across Resource-Limited Settings](https://mlsanity.com/paper/2404.02738/) (2024)
- [Federated Learning for Cross-Modality Medical Image Segmentation via Augmentation-Driven Generalization](https://mlsanity.com/paper/2602.20773/) (2026)
- [Decoupling Wavelet Sub-bands for Single Source Domain Generalization in Fundus Image Segmentation](https://mlsanity.com/paper/2603.28463/) (2026)
- [Frequency Adapter with SAM for Generalized Medical Image Segmentation](https://mlsanity.com/paper/2605.09925/) (2026)
- [GDBA-Net: A Gated Dual-Branch Adaptation of SAM2 for Robust Cross-Domain Nuclei Segmentation](https://mlsanity.com/paper/2610.20073/) (2026)

## Abstract (arXiv)

The annotation scarcity of medical image segmentation poses challenges in collecting sufficient training data for deep learning models. Specifically, models trained on limited data may not generalize well to other unseen data domains, resulting in a domain shift issue. Consequently, domain generalization (DG) is developed to boost the performance of segmentation models on unseen domains. However, the DG setup requires multiple source domains, which impedes the efficient deployment of segmentation algorithms in clinical scenarios. To address this challenge and improve the segmentation model's generalizability, we propose a novel approach called the Frequency-mixed Single-source Domain Generalization method (FreeSDG). By analyzing the frequency's effect on domain discrepancy, FreeSDG leverages a mixed frequency spectrum to augment the single-source domain. Additionally, self-supervision is constructed in the domain augmentation to learn robust context-aware representations for the segmentation task. Experimental results on five datasets of three modalities demonstrate the effectiveness of the proposed algorithm. FreeSDG outperforms state-of-the-art methods and significantly improves the segmentation model's generalizability. Therefore, FreeSDG provides a promising solution for enhancing the generalization of medical image segmentation models, especially when annotated data is scarce. The code is available at https://github.com/liamheng/Non-IID_Medical_Image_Segmentation.