用RF-DETR模型提升病理切片中分裂细胞的跨域检测鲁棒性。
RF-DETR for Robust Mitotic Figure Detection: A MIDOG 2025 Track 1 Approach
- 基于RF-DETR单阶段检测框架，结合困难负样本挖掘。
- 在预测试集上达F1 0.789，召回率0.839，泛化能力强。
- 适合需要高鲁棒性的医学图像检测任务开发者参考。
组织病理图像中的分裂细胞检测因不同扫描仪、染色方案和组织类型导致的显著领域差异而面临挑战。本文介绍我们在MIDOG 2025挑战赛第1赛道的解决方案，聚焦于跨多种组织学场景的稳健分裂细胞检测。原计划采用两阶段方法（先高召回检测，再分类精炼），但时间限制使我们转而优化单阶段检测流程。采用基于MIDOG++数据集训练的RF-DETR（Roboflow Detection Transformer）模型，并引入困难负样本挖掘。在预测试集上，该方法取得F1分数0.789，召回率0.839，精确率0.746，展现出对未见领域的良好泛化能力。本方案揭示了训练数据平衡与困难负样本挖掘在应对分裂细胞检测领域偏移问题中的重要性。
原文摘要 · Abstract (English)
Mitotic figure detection in histopathology images remains challenging due to significant domain shifts across different scanners, staining protocols, and tissue types. This paper presents our approach for the MIDOG 2025 challenge Track 1, focusing on robust mitotic figure detection across diverse histological contexts. While we initially planned a two-stage approach combining high-recall detection with subsequent classification refinement, time constraints led us to focus on optimizing a single-stage detection pipeline. We employed RF-DETR (Roboflow Detection Transformer) with hard negative mining, trained on MIDOG++ dataset. On the preliminary test set, our method achieved an F1 score of 0.789 with a recall of 0.839 and precision of 0.746, demonstrating effective generalization across unseen domains. The proposed solution offers insights into the importance of training data balance and hard negative mining for addressing domain shift challenges in mitotic figure detection.
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