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

• URL: http://arxiv.org/abs/2503.06563v1
• Date: Sun, 09 Mar 2025 11:33:59 GMT
• Title: LSA: Latent Style Augmentation Towards Stain-Agnostic Cervical Cancer Screening
• Authors: Jiangdong Cai, Haotian Jiang, Zhenrong Shen, Yonghao Li, Honglin Xiong, Lichi Zhang, Qian Wang,
• Abstract summary: Latent Style Augmentation (LSA) is a framework that performs efficient, online stain augmentation directly on WSI-level latent features.<n>We first introduce WSAug, a WSI-level stain augmentation method ensuring consistent stain across patches within a WSI.<n>Using offline-augmented WSIs by WSAug, we design and train Stain Transformer, which can simulate targeted style in the latent space.
• Score: 12.77650539024463
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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