AI for cervical cancer screening on whole slide images: opportunities with open-source simple tools

Cervical cancer remains a major global health burden, where early detection is critical. Cytological and histological assessments aim to identify precancerous squamous intraepithelial lesions (SILs). While artificial intelligence and machine learning have shown promise, most approaches rely on cytology or are not tailored for SIL classification. The aim of this study is to develop and evaluate a weakly supervised, pixel-level machine learning framework for the histological classification of low grade and high grade SIL in whole slide images (WSIs). Specifically, we sought to assess whether an open source segmentation pipeline trained on sparsely annotated WSIs could accurately support slide-level diagnostic interpretation while minimizing annotation burden and maintaining clinical interpretability.

We propose a weakly supervised machine learning framework for classifying low grade and high grade SILs in whole-slide histological images. Using Random Forest classifiers for pixel-level segmentation, the system mimics pathologists by quantifying tissue components. Training required only sparse annotations from a limited set of WSIs, yielding millions of pixel-level samples and reducing annotation burden.

Applied on a test set of 309 cervical WSIs, the system achieved over 96% concordance with expert pathologists, correctly distinguishing low grade LSIL, high grade HSIL, and normal epithelium, with only one false negative and a 7-10 false positives, depending on the used model.

Our approach offers accurate, interpretable, and low-cost diagnostic support, with potential for integration into routine workflows, especially in resource-limited settings.