提升病理图像中分裂细胞检测的实时性与泛化能力
A bag of tricks for real-time Mitotic Figure detection
- 基于RTMDet构建单阶段检测器，结合多域数据与增强策略应对设备差异
- 在多个数据集上实现0.78-0.84的F1分数，MIDOG挑战赛达0.81
- 适合临床部署，兼顾速度与准确率，尤其适用于新域适应场景
由于扫描仪差异、染色协议、组织类型及伪影等因素，病理图像中的分裂细胞（MF）检测极具挑战。本文提出一套训练技巧组合，使模型在多样域下实现鲁棒且实时的MF检测。基于高效的单阶段检测器RTMDet，实现适合临床部署的高推理速度。通过大规模多域训练数据、平衡采样和精心设计的增强策略，缓解扫描仪差异与肿瘤异质性问题。针对坏死与碎片组织，采用针对性硬负样本挖掘，显著降低误检率。在多个MF数据集上的分组5折交叉验证中，模型F1分数介于0.78至0.84之间；在MItosis DOmain Generalization（MIDOG）2025挑战赛初步测试集上，基于RTMDet-S的单阶段方法取得0.81的F1分数，优于更大模型，展现出对未知域的强大适应能力。该方案在精度与速度间取得实用平衡，利于真实临床应用。
原文摘要 · Abstract (English)
Mitotic figure (MF) detection in histopathology images is challenging due to large variations in slide scanners, staining protocols, tissue types, and the presence of artifacts. This paper presents a collection of training techniques - a bag of tricks - that enable robust, real-time MF detection across diverse domains. We build on the efficient RTMDet single stage object detector to achieve high inference speed suitable for clinical deployment. Our method addresses scanner variability and tumor heterogeneity via extensive multi-domain training data, balanced sampling, and careful augmentation. Additionally, we employ targeted, hard negative mining on necrotic and debris tissue to reduce false positives. In a grouped 5-fold cross-validation across multiple MF datasets, our model achieves an F1 score between 0.78 and 0.84. On the preliminary test set of the MItosis DOmain Generalization (MIDOG) 2025 challenge, our single-stage RTMDet-S based approach reaches an F1 of 0.81, outperforming larger models and demonstrating adaptability to new, unfamiliar domains. The proposed solution offers a practical trade-off between accuracy and speed, making it attractive for real-world clinical adoption.
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