LSA: Latent Style Augmentation Towards Stain-Agnostic Cervical Cancer Screening
Journal:
arXiv
Published Date:
Mar 9, 2025
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.
