用集成策略提升宫颈癌细胞检测准确率，解决重叠与类别不平衡问题。
Detection and Classification of (Pre)Cancerous Cells in Pap Smears: An Ensemble Strategy for the RIVA Cervical Cytology Challenge
- 基于YOLOv11m，结合损失加权、数据重采样和迁移学习三策略
- 集成模型在最终测试集上达到0.147的mAP50-95，比最优单模型高29%
- 适合医学影像分析、宫颈癌筛查自动化研究者参考
自动化检测与分类常规巴氏涂片中的宫颈细胞可大规模提升宫颈癌筛查效率，减轻人工负担，提高分诊一致性。但受严重类别不平衡和核重叠问题制约。本文针对ISBI 2026年RIVA宫颈细胞学挑战赛，提出多类检测八种贝斯蒂斯细胞类型的方法。以YOLOv11m为基础架构，系统评估三种优化策略：损失重加权、数据重采样与迁移学习。通过融合各策略训练的模型，并采用加权框融合（WBF）进行集成，实现互补检测行为。该集成模型在预测试集上取得0.201的mAP50-95，最终测试集为0.147，相较最优单模型提升29%，验证了组合多种不平衡缓解策略的有效性。
原文摘要 · Abstract (English)
Automated detection and classification of cervical cells in conventional Pap smear images can strengthen cervical cancer screening at scale by reducing manual workload, improving triage, and increasing consistency across readers. However, it is challenged by severe class imbalance and frequent nuclear overlap. We present our approach to the RIVA Cervical Cytology Challenge (ISBI 2026), which requires multi-class detection of eight Bethesda cell categories under these conditions. Using YOLOv11m as the base architecture, we systematically evaluate three strategies to improve detection performance: loss reweighting, data resampling and transfer learning. We build an ensemble by combining models trained under each strategy, promoting complementary detection behavior and combining them through Weighted Boxes Fusion (WBF). The ensemble achieves a mAP50-95 of 0.201 on the preliminary test set and 0.147 on the final test set, representing a 29% improvement over the best individual model on the final test set and demonstrating the effectiveness of combining complementary imbalance mitigation strategies.
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