Abstract
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.
Abstract (translated)
深度学习在包括计算病理学（CPath）在内的多个领域取得了显著进展。然而，由于领域迁移问题的复杂性，现有模型的表现会下降，特别是在处理多域或跨域任务时尤为明显。在这篇论文中，我们提出了一种测试时间风格转换（T3s），该方法采用双向映射机制将源域和目标域的特征投影到统一的特征空间中，从而增强模型的泛化能力。为了进一步扩大风格表达空间，我们引入了跨域风格多样化模块（CSDM）来确保风格基线之间的正交性。此外，我们还使用数据增强和低秩自适应技术来改进特征对齐和敏感度，使模型能够有效应对多域输入。我们的方法在三个未见过的数据集上展示了其有效性。
URL
https://arxiv.org/abs/2503.18567
