# Domain Game: Disentangle Anatomical Feature for Single Domain Generalized Segmentation

arXiv: https://arxiv.org/abs/2406.02125 · HTML: https://mlsanity.com/paper/2406.02125/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 34 references that are in the map.
- First posted on arXiv 4 Jun 2024.

## Builds on (most cited first, 23 of 23)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification](https://mlsanity.com/paper/2107.02314/) (2021)
- [Generalizing to Unseen Domains via Adversarial Data Augmentation](https://mlsanity.com/paper/1805.12018/) (2018)
- [Domain Generalization via Model-Agnostic Learning of Semantic Features](https://mlsanity.com/paper/1910.13580/) (2019)
- [Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net](https://mlsanity.com/paper/1807.09441/) (2018)
- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [A Geometric Analysis of Neural Collapse with Unconstrained Features](https://mlsanity.com/paper/2105.02375/) (2021)
- [Robust and Generalizable Visual Representation Learning via Random Convolutions](https://mlsanity.com/paper/2007.13003/) (2020)
- [Uncertainty Modeling for Out-of-Distribution Generalization](https://mlsanity.com/paper/2202.03958/) (2022)
- [Causality-inspired Single-source Domain Generalization for Medical Image Segmentation](https://mlsanity.com/paper/2111.12525/) (2021)
- [The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa)](https://mlsanity.com/paper/2305.19369/) (2023)
- [The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)](https://mlsanity.com/paper/2305.17033/) (2023)
- [BayeSeg: Bayesian Modeling for Medical Image Segmentation with Interpretable Generalizability](https://mlsanity.com/paper/2303.01710/) (2023)
- [DisC-Diff: Disentangled Conditional Diffusion Model for Multi-Contrast MRI Super-Resolution](https://mlsanity.com/paper/2303.13933/) (2023)
- [Adversarial Consistency for Single Domain Generalization in Medical Image Segmentation](https://mlsanity.com/paper/2206.13737/) (2022)
- [CDDSA: Contrastive Domain Disentanglement and Style Augmentation for Generalizable Medical Image Segmentation](https://mlsanity.com/paper/2211.12081/) (2022)
- [Domain Shift in Computer Vision models for MRI data analysis: An Overview](https://mlsanity.com/paper/2010.07222/) (2020)
- [CELLO-3D: Estimating the Covariance of ICP in the Real World](https://mlsanity.com/paper/1810.01470/) (2018)
- [Robust White Matter Hyperintensity Segmentation on Unseen Domain](https://mlsanity.com/paper/2102.06650/) (2021)
- [Multi-view Self-supervised Disentanglement for General Image Denoising](https://mlsanity.com/paper/2309.05049/) (2023)

## Built on by (most cited first, 3 of 3)

- [Gastric-X: A Multimodal Multi-Phase Benchmark Dataset for Advancing Vision-Language Models in Gastric Cancer Analysis](https://mlsanity.com/paper/2603.19516/) (2026)
- [Low-Rank Adaptive Structural Priors for Generalizable Diabetic Retinopathy Grading](https://mlsanity.com/paper/2504.19362/) (2025)
- [Exploring Image Representation with Decoupled Classical Visual Descriptors](https://mlsanity.com/paper/2510.14536/) (2025)

## Abstract (arXiv)

Single domain generalization aims to address the challenge of out-of-distribution generalization problem with only one source domain available. Feature distanglement is a classic solution to this purpose, where the extracted task-related feature is presumed to be resilient to domain shift. However, the absence of references from other domains in a single-domain scenario poses significant uncertainty in feature disentanglement (ill-posedness). In this paper, we propose a new framework, named textitDomain Game, to perform better feature distangling for medical image segmentation, based on the observation that diagnostic relevant features are more sensitive to geometric transformations, whilist domain-specific features probably will remain invariant to such operations. In domain game, a set of randomly transformed images derived from a singular source image is strategically encoded into two separate feature sets to represent diagnostic features and domain-specific features, respectively, and we apply forces to pull or repel them in the feature space, accordingly. Results from cross-site test domain evaluation showcase approximately an ~11.8% performance boost in prostate segmentation and around ~10.5% in brain tumor segmentation compared to the second-best method.