用于组织病理学图像多域有丝分裂象检测的集成YOLO框架
Ensemble YOLO Framework for Multi-Domain Mitotic Figure Detection in Histopathology Images
AI总结：
本文提出集成YOLOv5与YOLOv8的框架，利用锚框与无锚框检测器的互补优势，在MIDOG等多域数据集上提升有丝分裂象检测的泛化性与F1分数。
摘要：
在全切片组织病理学图像中可靠识别有丝分裂象仍然困难，原因在于其出现率低、形态异质性显著，以及组织处理和染色程序引入的不一致性。MIDOG竞赛系列为评估跨不同域的检测方法提供了标准化基准，从而推动了可泛化深度学习模型的发展。在本工作中，我们研究了两种现代单阶段检测器YOLOv5和YOLOv8的性能，它们在MIDOG++、CMC和CCMCT数据集上进行了训练。为增强鲁棒性，训练中加入了染色不变的色彩扰动和保留纹理的增强。在内部验证中，YOLOv5取得了更高的精确率（84.3%），而YOLOv8提供了更好的召回率（82.6%），反映了基于锚框与无锚框检测之间的架构权衡。为利用它们的互补优势，我们采用了这两个模型的集成，在保持有竞争力精确率的同时提高了灵敏度（85.3%），获得了83.1%的最佳F1分数。在初步的MIDOG 2025测试排行榜上，我们的集成方法以79.2%的F1分数、73.6%的精确率和85.8%的召回率排名第5，证实了所提策略在未见测试数据上有效泛化。这些发现凸显了结合基于锚框和无锚框目标检测器在推进数字病理学自动有丝分裂检测方面的有效性。
英文摘要：
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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