提出一种数据驱动的有丝分裂检测训练方法，提升模型效率与精度。
Sequential Hard Mining: a data-centric approach for Mitosis Detection
- 基于提升思想设计序列化困难样本挖掘策略
- 在MIDOG 2025挑战赛中显著提升检测性能
- 适合关注医学图像标注效率的研究者
随着组织学图像中有丝分裂图像标注数据集的持续增长，如何高效利用这些前所未有的数据量来最优训练深度学习模型已成为新挑战。本文基于先前方法，聚焦受提升技术启发的高效训练数据采样策略，提出针对MIDOG 2025挑战赛两个赛道的候选解决方案。
原文摘要 · Abstract (English)
With a continuously growing availability of annotated datasets of mitotic figures in histology images, finding the best way to optimally use with this unprecedented amount of data to optimally train deep learning models has become a new challenge. Here, we build upon previously proposed approaches with a focus on efficient sampling of training data inspired by boosting techniques and present our candidate solutions for the two tracks of the MIDOG 2025 challenge.
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