Cell-Level Free Cervical Lesion Detection in Cytology Images Via Weakly Supervised Self-Correction

Cervical cancer remains the fourth most common cancer among women worldwide. Early detection of cervical lesions in cytology images can prevent disease progression, but current deep learning methods for cell- or patch-level analysis in whole slide images (WSI) face significant challenges due to limited, noisy, or incomplete annotations. To address these limitations, weakly supervised learning methods, particularly multiple instance learning (MIL), have been explored. However, traditional MIL methods often suffer from label noise, leading to inaccurate feature extraction, which in turn restricts their robustness and generalization. In this paper, we propose Self-Correcting Instance Learning (SCIL), a novel two-stage instance-based MIL framework designed to enhance instance-level cervical lesion detection under bag-level supervision. SCIL incorporates a weakly supervised self-correction mechanism within a teacher-student architecture to mitigate the effects of noisy pseudo labels. This process involves a contrastive dynamic weighting strategy to adjust instance-level loss and enhance feature representation in stage one, followed by an uncertainty-based self-correction strategy in stage two to retain only high-confidence data with reassigned labels. Extensive evaluations of a slide cervical cytology image dataset demonstrate that SCIL significantly improves the detection of cervical lesions at both the patch and slide levels, highlighting its ability to overcome the limitations of imperfect data in cervical lesion detection.