用概率建模+对比学习，让医学图像分割更抗域偏移
From Deterministic to Probabilistic: A Novel Perspective on Domain Generalization for Medical Image Segmentation
- 融合确定性特征与不确定性建模，捕捉完整特征分布
- 通过均值与协方差对齐，实现分布级特征对齐
- 适合医学图像分割中跨数据集泛化场景
传统领域泛化方法依赖域对齐以减少域间分布差异并学习域不变表示，但域偏移本质难以消除，限制了模型泛化能力。为此，本文提出一种新框架，通过概率建模与对比学习提升数据表示质量，降低对域对齐的依赖，并增强在域变化下的鲁棒性。具体地，将确定性特征与不确定性建模结合，以捕获全面的特征分布；对比学习通过对齐特征分布的均值与协方差，实现分布级对齐，使模型能动态适应域变化、缓解分布偏移。此外，设计基于离散小波变换的频域结构增强策略，保留关键结构细节，减少风格变化带来的视觉失真。实验表明，该框架显著提升分割性能，为医学图像分割中的域泛化问题提供稳健解决方案。
原文摘要 · Abstract (English)
Traditional domain generalization methods often rely on domain alignment to reduce inter-domain distribution differences and learn domain-invariant representations. However, domain shifts are inherently difficult to eliminate, which limits model generalization. To address this, we propose an innovative framework that enhances data representation quality through probabilistic modeling and contrastive learning, reducing dependence on domain alignment and improving robustness under domain variations. Specifically, we combine deterministic features with uncertainty modeling to capture comprehensive feature distributions. Contrastive learning enforces distribution-level alignment by aligning the mean and covariance of feature distributions, enabling the model to dynamically adapt to domain variations and mitigate distribution shifts. Additionally, we design a frequency-domain-based structural enhancement strategy using discrete wavelet transforms to preserve critical structural details and reduce visual distortions caused by style variations. Experimental results demonstrate that the proposed framework significantly improves segmentation performance, providing a robust solution to domain generalization challenges in medical image segmentation.
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