arXiv 2503.065632025-03-09eess.IVcs.AIcs.CV

LSA：面向染色无关宫颈癌筛查的潜在风格增强

LSA: Latent Style Augmentation Towards Stain-Agnostic Cervical Cancer Screening

Shanghai Clinical Research and Trial Center（上海临床研究与试验中心）；School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）

Jiangdong Cai, Haotian Jiang, Zhenrong Shen, Yonghao Li, Honglin Xiong, Lichi Zhang, Qian Wang

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AI总结：

本文提出潜在风格增强框架LSA，通过全切片级染色增强方法WSAug和Stain Transformer在潜在空间实现在线风格模拟，解决了宫颈癌筛查中跨扫描仪染色域偏移问题，并在多扫描仪数据上验证了显著的分布外泛化性能提升。

摘要：

使用全切片图像（WSIs）的宫颈癌筛查计算机辅助诊断系统在部署时面临关键挑战，这些挑战源于不同扫描仪和成像环境之间的染色差异所导致的域偏移。现有的染色增强方法虽然提高了补丁级别的鲁棒性，但由于两个关键局限而无法扩展到全切片图像：（1）将补丁操作扩展到千兆像素级切片时会出现不一致的染色模式，以及（2）对增强后的全切片图像进行离线处理会带来难以承受的计算和存储成本。为了解决这一问题，我们提出了潜在风格增强（LSA）框架，该框架直接在全切片图像级别的潜在特征上执行高效的在线染色增强。我们首先引入WSAug，这是一种全切片图像级别的染色增强方法，可确保同一全切片图像内各补丁之间的染色一致性。利用WSAug离线增强的全切片图像，我们设计并训练了Stain Transformer，它能够在潜在空间中模拟目标风格，从而高效地增强全切片图像级别分类器的鲁棒性。我们在一个用于宫颈癌诊断的多扫描仪全切片图像数据集上验证了我们的方法。尽管仅使用来自单一扫描仪的数据进行训练，我们的方法在来自其他扫描仪的分布外数据上仍取得了显著的性能提升。代码将在https://github.com/caijd2000/LSA发布。

英文摘要：

The deployment of computer-aided diagnosis systems for cervical cancer screening using whole slide images (WSIs) faces critical challenges due to domain shifts caused by staining variations across different scanners and imaging environments. While existing stain augmentation methods improve patch-level robustness, they fail to scale to WSIs due to two key limitations: (1) inconsistent stain patterns when extending patch operations to gigapixel slides, and (2) prohibitive computational/storage costs from offline processing of augmented WSIs.To address this, we propose Latent Style Augmentation (LSA), a framework that performs efficient, online stain augmentation directly on WSI-level latent features. We first introduce WSAug, a WSI-level stain augmentation method ensuring consistent stain across patches within a WSI. Using offline-augmented WSIs by WSAug, we design and train Stain Transformer, which can simulate targeted style in the latent space, efficiently enhancing the robustness of the WSI-level classifier. We validate our method on a multi-scanner WSI dataset for cervical cancer diagnosis. Despite being trained on data from a single scanner, our approach achieves significant performance improvements on out-of-distribution data from other scanners. Code will be available at https://github.com/caijd2000/LSA.

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