提出在线潜空间染色增强，让宫颈癌筛查模型更抗染色差异。
LSA: Latent Style Augmentation Towards Stain-Agnostic Cervical Cancer Screening
- 在潜空间直接进行染色增强，避免图像级操作不一致。
- 单扫描仪训练下，跨扫描仪测试准确率提升显著。
- 适合医学图像分析、需跨设备泛化的研究者使用。
基于全切片图像（WSI）的宫颈癌辅助诊断系统部署面临染色差异带来的领域偏移挑战。现有染色增强方法虽能提升局部块的鲁棒性，但因两个关键限制难以推广至WSI：（1）将块级操作扩展到千兆像素级切片时染色模式不一致；（2）离线处理增强后的WSI带来巨大的计算与存储开销。为此，我们提出潜空间染色增强（LSA）框架，实现对WSI级潜在特征的高效在线染色增强。首先提出WSAug，一种保证同一WSI内各块染色一致性的大尺度染色增强方法。利用离线增强的WSI训练Stain Transformer，可在潜空间模拟目标风格，有效提升分类器的全局鲁棒性。在多扫描仪宫颈癌诊断数据集上验证，即使仅在单一扫描仪数据上训练，本方法在其他扫描仪的分布外数据上仍取得显著性能提升。代码将开源于https://github.com/caijd2000/LSA。
原文摘要 · Abstract (English)
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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