Abstract
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.
Abstract (translated)
基于传统巴氏涂片图像的宫颈细胞自动检测与分类可通过减少人工工作量、改善分流诊断并提升阅片一致性，从而加强大规模宫颈癌筛查。然而，该方法面临严重的类别不平衡和频繁的细胞核重叠挑战。本文介绍了我们针对RIVA宫颈细胞学挑战赛（ISBI 2026）的研究方案，该赛事要求在所述条件下对八类贝塞斯达细胞分类进行多类检测。以YOLOv11m为基础架构，我们系统评估了三种提升检测性能的策略：损失函数重加权、数据重采样与迁移学习。通过整合各策略训练的模型构建集成模型，促进互补检测行为，并采用加权框融合（WBF）进行结果合并。该集成模型在初步测试集上达到0.201的mAP50-95，在最终测试集上达到0.147，较最终测试集上的最佳单一模型提升29%，证明了结合互补性不平衡缓解策略的有效性。
URL
https://arxiv.org/abs/2603.23742
