融合两种YOLO模型提升病理切片中分裂细胞检测的准确率与鲁棒性。
Ensemble YOLO Framework for Multi-Domain Mitotic Figure Detection in Histopathology Images
- 采用双模型集成策略，结合锚框依赖与无锚框检测优势。
- 在未见数据上达到85.8%召回率，F1分数达79.2%。
- 适用于需高灵敏度的医学图像分析场景，如癌症诊断辅助。
全片组织病理图像中可靠识别有丝分裂细胞仍具挑战，因其出现率低、形态差异大，且受组织处理与染色不一致影响。MIDOG竞赛系列提供了跨领域评估检测方法的标准基准，推动通用深度学习模型的发展。本文研究了YOLOv5与YOLOv8两种现代单阶段检测器在MIDOG++、CMC和CCMCT数据集上的表现。训练中引入染色不变的颜色扰动与保留纹理的增强。内部验证显示，YOLOv5精度更高（84.3%），而YOLOv8召回率更优（82.6%），反映锚框依赖与无锚框架构的权衡。为发挥互补优势，采用两模型集成，提升敏感性至85.3%，维持良好精度，获得最佳F1分数83.1%。在MIDOG 2025初步测试榜单上，集成模型排名第五，F1分数79.2%，精度73.6%，召回85.8%，证实该策略在未见数据上具有强泛化能力。结果表明，结合锚框依赖与无锚框检测器可有效推进数字病理学中的自动化有丝分裂检测。
原文摘要 · Abstract (English)
The reliable identification of mitotic figures in whole-slide histopathological images remains difficult, owing to their low prevalence, substantial morphological heterogeneity, and the inconsistencies introduced by tissue processing and staining procedures. The MIDOG competition series provides standardized benchmarks for evaluating detection approaches across diverse domains, thus motivating the development of generalizable deep learning models. In this work, we investigate the performance of two modern one-stage detectors, YOLOv5 and YOLOv8, trained on MIDOG++, CMC, and CCMCT datasets. To enhance robustness, training incorporated stain-invariant color perturbations and texture-preserving augmentations. Ininternal validation, YOLOv5 achieved higher precision (84.3%), while YOLOv8 offered improved recall (82.6%), reflecting architectural trade-offs between anchor-based and anchor-free detections. To capitalize on their complementary strengths, weemployed an ensemble of the two models, which improved sensitivity (85.3%) while maintaining competitive precision, yielding the best F1 score of 83.1%. On the preliminary MIDOG 2025 test leaderboard, our ensemble ranked 5th with an F1 score of 79.2%, precision of 73.6%, and recall of 85.8%, confirming that the proposed strategy generalizes effectively across unseen test data. These findings highlight the effectiveness of combining anchor-based and anchor-free object detectors to advance automated mitosis detection in digital pathology.
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