# Treasure in Distribution: A Domain Randomization based Multi-Source Domain Generalization for 2D Medical Image Segmentation

arXiv: https://arxiv.org/abs/2305.19949 · HTML: https://mlsanity.com/paper/2305.19949/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 25 references that are in the map.
- First posted on arXiv 31 May 2023.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021)
- [Domain Generalization: A Survey](https://mlsanity.com/paper/2103.02503/) (2021)
- [Domain Generalization with MixStyle](https://mlsanity.com/paper/2104.02008/) (2021)
- [Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net](https://mlsanity.com/paper/1807.09441/) (2018)
- [REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs](https://mlsanity.com/paper/1910.03667/) (2019)
- [RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening](https://mlsanity.com/paper/2103.15597/) (2021)
- [Robust and Generalizable Visual Representation Learning via Random Convolutions](https://mlsanity.com/paper/2007.13003/) (2020)
- [Deep Semantic Segmentation of Natural and Medical Images: A Review](https://mlsanity.com/paper/1910.07655/) (2019)
- [Uncertainty Modeling for Out-of-Distribution Generalization](https://mlsanity.com/paper/2202.03958/) (2022)
- [Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains](https://mlsanity.com/paper/2007.02035/) (2020)
- [Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalization](https://mlsanity.com/paper/2203.07740/) (2022)
- [Switchable Whitening for Deep Representation Learning](https://mlsanity.com/paper/1904.09739/) (2019)
- [Semantic-Aware Domain Generalized Segmentation](https://mlsanity.com/paper/2204.00822/) (2022)
- [Batch Normalization Embeddings for Deep Domain Generalization](https://mlsanity.com/paper/2011.12672/) (2020)
- [Global and Local Texture Randomization for Synthetic-to-Real Semantic Segmentation](https://mlsanity.com/paper/2108.02376/) (2021)
- [Domain and Content Adaptive Convolution based Multi-Source Domain Generalization for Medical Image Segmentation](https://mlsanity.com/paper/2109.05676/) (2021)
- [MaxStyle: Adversarial Style Composition for Robust Medical Image Segmentation](https://mlsanity.com/paper/2206.01737/) (2022)

## Built on by (most cited first, 15 of 15)

- [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)
- [CoSAM: Self-Correcting SAM for Domain Generalization in 2D Medical Image Segmentation](https://mlsanity.com/paper/2411.10136/) (2024)
- [MRGen: Segmentation Data Engine for Underrepresented MRI Modalities](https://mlsanity.com/paper/2412.04106/) (2024)
- [Meta Curvature-Aware Minimization for Domain Generalization](https://mlsanity.com/paper/2412.11542/) (2024)
- [Style Content Decomposition-based Data Augmentation for Domain Generalizable Medical Image Segmentation](https://mlsanity.com/paper/2502.20619/) (2025)
- [LangDAug: Langevin Data Augmentation for Multi-Source Domain Generalization in Medical Image Segmentation](https://mlsanity.com/paper/2505.19659/) (2025)
- [ConStyX: Content Style Augmentation for Generalizable Medical Image Segmentation](https://mlsanity.com/paper/2506.10675/) (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)
- [Casual Inference via Style Bias Deconfounding for Domain Generalization](https://mlsanity.com/paper/2503.16852/) (2025)
- [Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation](https://mlsanity.com/paper/2507.23326/) (2025)
- [Adversarial Batch Representation Augmentation for Batch Correction in High-Content Cellular Screening](https://mlsanity.com/paper/2603.05622/) (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)
- [CoDG-Net: Structure-Guided Style Diffusion and Collaborative Learning to Mitigate Catastrophic Forgetting in Medical Image Domain Generalization](https://mlsanity.com/paper/2610.05053/) (2026)

## Abstract (arXiv)

Although recent years have witnessed the great success of convolutional neural networks (CNNs) in medical image segmentation, the domain shift issue caused by the highly variable image quality of medical images hinders the deployment of CNNs in real-world clinical applications. Domain generalization (DG) methods aim to address this issue by training a robust model on the source domain, which has a strong generalization ability. Previously, many DG methods based on feature-space domain randomization have been proposed, which, however, suffer from the limited and unordered search space of feature styles. In this paper, we propose a multi-source DG method called Treasure in Distribution (TriD), which constructs an unprecedented search space to obtain the model with strong robustness by randomly sampling from a uniform distribution. To learn the domain-invariant representations explicitly, we further devise a style-mixing strategy in our TriD, which mixes the feature styles by randomly mixing the augmented and original statistics along the channel wise and can be extended to other DG methods. Extensive experiments on two medical segmentation tasks with different modalities demonstrate that our TriD achieves superior generalization performance on unseen target-domain data. Code is available at https://github.com/Chen-Ziyang/TriD.