A cell comparative multiple instance learning network guided by image quality assessment for cervical whole slide image classification

Early screening is essential for reducing the incidence and mortality of cervical cancer, and artificial intelligence-based analysis of whole slide images (WSIs) enables large-scale automated screening. However, existing methods often ignore image quality variations and inter-individual morphological differences, which limits their robustness in clinical settings. This study proposes a quality-aware cervical WSI classification framework that integrates image quality assessment with pathologist-inspired normal-abnormal cell comparison. A quality evaluation module filters unreliable patches, while a cell comparison and enhancement strategy enlarges the feature discrepancy between normal and abnormal cells to mitigate individual variability. Supervised contrastive learning further strengthens abnormal cell discrimination, and patch-level quality scores are incorporated into an attention-based multiple instance learning framework to guide WSI classification. Experiments on 2,434 WSIs from five medical institutions demonstrate that our method achieves superior performance in real-world scenarios, significantly outperforming state-of-the-art methods by 1.93% in average overall accuracy.