测试时通过风格迁移提升跨器官模型泛化能力
Advancing Cross-Organ Domain Generalization with Test-Time Style Transfer and Diversity Enhancement
- 测试时用双向映射将不同域特征对齐到统一空间
- 引入正交风格基增强风格表达，跨域泛化性能显著提升
- 适合医学图像跨器官分析，尤其在无训练数据场景
深度学习在计算病理学等领域取得显著进展，但域偏移问题导致模型在多域或跨域任务中性能下降。本文提出测试时风格迁移（T3s），通过双向映射机制将源域与目标域特征投影至统一特征空间，提升模型泛化能力。为进一步扩大风格表达空间，设计跨域风格多样性模块（CSDM），确保风格基之间的正交性。同时结合数据增强与低秩适应技术，优化特征对齐与敏感度，使模型能有效适应多域输入。方法在三个未见数据集上验证有效。
原文摘要 · Abstract (English)
Deep learning has made significant progress in addressing challenges in various fields including computational pathology (CPath). However, due to the complexity of the domain shift problem, the performance of existing models will degrade, especially when it comes to multi-domain or cross-domain tasks. In this paper, we propose a Test-time style transfer (T3s) that uses a bidirectional mapping mechanism to project the features of the source and target domains into a unified feature space, enhancing the generalization ability of the model. To further increase the style expression space, we introduce a Cross-domain style diversification module (CSDM) to ensure the orthogonality between style bases. In addition, data augmentation and low-rank adaptation techniques are used to improve feature alignment and sensitivity, enabling the model to adapt to multi-domain inputs effectively. Our method has demonstrated effectiveness on three unseen datasets.
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