针对病理图像中分裂细胞检测的跨域难题，提出两阶段检测方法。
Challenges and Lessons from MIDOG 2025: A Two-Stage Approach to Domain-Robust Mitotic Figure Detection
- 先用Faster R-CNN定位候选区域，再用三个模型集成过滤假阳性。
- 最高召回率达95.28%，但精确率仅12.67%，显示误检严重。
- 揭示跨域泛化难问题，适合关注病理图像算法落地的研究者。
由于组织域差异和形态复杂性，分裂细胞检测在计算病理学中仍具挑战。本文介绍在MIDOG 2025挑战赛中的参与经验，聚焦跨域鲁棒检测。我们设计了两阶段流程：第一阶段使用Faster R-CNN在仅MIDOG++数据集上训练，生成候选区域；第二阶段采用DenseNet-121、EfficientNet-v2与InceptionResNet-v2三模型集成进行假阳性剔除。最佳提交结果为F1分数0.2237（召回率0.9528，精确率0.1267）。高召回表明能有效捕获真实分裂细胞，但精确率仅12.67%暴露了在不同组织域间区分真伪分裂细胞的根本困难。六种变体分析显示后续优化反而恶化性能，凸显组织病理学中域泛化的复杂性。本工作提供了构建鲁棒分裂细胞检测算法的实际挑战洞察，并强调抑制假阳性的关键作用。
原文摘要 · Abstract (English)
Mitotic figure detection remains a challenging task in computational pathology due to domain variability and morphological complexity. This paper describes our participation in the MIDOG 2025 challenge, focusing on robust mitotic figure detection across diverse tissue domains. We developed a two-stage pipeline combining Faster R-CNN for candidate detection with an ensemble of three classifiers (DenseNet-121, EfficientNet-v2, InceptionResNet-v2) for false positive reduction. Our best submission achieved F1-score 0.2237 (Recall: 0.9528, Precision: 0.1267) using a Faster R-CNN trained solely on MIDOG++ dataset. While our high recall demonstrates effective mitotic figure detection, the critically low precision (12.67%) reveals fundamental challenges in distinguishing true mitoses from morphologically similar imposters across diverse domains. Analysis of six submission variants showed that subsequent optimization attempts were counterproductive, highlighting the omplexity of domain generalization in histopathology. This work provides valuable insights into the practical challenges of developing robust mitotic figure detection algorithms and emphasizes the importance of effective false positive suppression strategies.
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