病理AI团队和计算病理学研究者注意了：MIDOG 2025揭示了当前有丝分裂检测模型在真实世界中的脆弱性，尤其是罕见肿瘤类型和困难区域。如果你的模型只在热点区域表现好，点开看看盲点在哪，以及集成方法如何带来稳定提升。
MIDOG 2025挑战赛旨在评估有丝分裂检测算法在真实世界中的泛化能力，超越了以往仅关注扫描仪差异的基准。挑战赛构建了包含12种人类、犬类和猫类肿瘤类型、365个病例的测试数据集，并引入了随机组织区域和困难区域检测，以及非典型有丝分裂图分类任务。结果显示，在传统热点区域表现良好的模型在困难区域性能显著下降，假阳性率增加三倍，且在不同肿瘤类型间表现差异巨大，揭示了当前模型的“盲点”。集成方法平均提升了F1分数1.5个百分点和平衡准确率1.3个百分点，而测试时增强无明显改善。该挑战表明，真实世界的有丝分裂检测仍是重大挑战，多情境评估框架为临床可靠性提供了更现实的代理指标。
Mitosis Detection in the Wild: Multi-Tumor and Context-Aware Generalization in the MIDOG 2025 Challenge
Automated mitosis detection is a well-established task in computational pathology. While previous benchmarks focused on scanner-induced domain shift, clinical "real-world" application requires models to be robust across the vast variance to be expected in the histological landscape. The MItosis DOmain Generalization (MIDOG) 2025 challenge was designed to evaluate algorithmic performance across unprecedented biological and contextual diversity. We curated a test dataset of 365 cases, encompassing 12 distinct human, canine and feline tumor types, digitized across multiple scanning platforms. Moving beyond hand-selected hotspots, the challenge required detection also in random tissue areas (representative of the whole slide detection situation) and challenging areas (areas rich in hard negatives). In the second track, we introduced the classification of atypical mitotic figures (AMFs). There were 18 teams submitting to the detection track, with F1 scores ranging up to 0.740. In the AMF detection track, we had 21 submissions with balanced accuracy values up to 0.908. Our analysis reveals that while most models perform reliably in traditional hotspots, significant performance degradation occurs in challenging ROIs, where false positive rates tripled. Furthermore, performance varied significantly across the 12 tumor types, highlighting "blind spots" in current state-of-the-art architectures when encountering rare or highly pleomorphic malignancies. Moreover, we evaluated the effectiveness of ensembling and found a mean increases of 1.5 and 1.3 percentage points in F1 score and balanced accuracy, respectively. In contrast, TTA showed no relevant improvement. MIDOG 2025 demonstrates that "in the wild" mitosis detection remains a significant hurdle. The transition from hotspot-only evaluation to a multi-contextual framework provides a more realistic proxy for clinical reliability.