用YOLOv12实现跨癌症类型稳定检测有丝分裂图像，提升病理诊断一致性。
Robust Pan-Cancer Mitotic Figure Detection with YOLOv12
- 基于YOLOv12架构，端到端检测有丝分裂图像
- 在复杂切片上达F1=0.7216，热点区域达F1=0.801
- 无需外部数据，适用于多种癌症病理分析
有丝分裂图像是肿瘤病理中的关键预后特征，能反映肿瘤侵袭性和增殖活性。然而其识别仍具挑战，即使经验丰富的病理科医生也存在显著判断差异。为应对这一问题，MItosis DOmain Generalization（MIDOG）2025挑战赛作为第三届国际竞赛，旨在开发鲁棒的有丝分裂检测算法。本文提出一种基于前沿YOLOv12目标检测架构的有丝分裂检测方法，在初步测试集（仅热点区域）上取得0.801的F1分数，最终测试排行榜上以0.7216的F1分数位列第二，覆盖复杂且异质的全切片区域，且未依赖外部数据。
原文摘要 · Abstract (English)
Mitotic figures represent a key histoprognostic feature in tumor pathology, providing crucial insights into tumor aggressiveness and proliferation. However, their identification remains challenging, subject to significant inter-observer variability, even among experienced pathologists. To address this issue, the MItosis DOmain Generalization (MIDOG) 2025 challenge marks the third edition of an international competition aiming to develop robust mitosis detection algorithms. In this paper, we present a mitotic figure detection approach based on the state-of-the-art YOLOv12 object detection architecture. Our method achieved an F1-score of 0.801 on the preliminary test set (hotspots only) and ranked second on the final test leaderboard with an F1-score of 0.7216 across complex and heterogeneous whole-slide regions, without relying on external data.
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