Mitosis detection is one of the fundamental tasks in computational pathology, which is extremely challenging due to the heterogeneity of mitotic cell. Most of the current studies solve the heterogeneity in the technical aspect by increasing the model complexity. However, lacking consideration of the biological knowledge and the complex model design may lead to the overfitting problem while limited the generalizability of the detection model. In this paper, we systematically study the morphological appearances in different mitotic phases as well as the ambiguous non-mitotic cells and identify that balancing the data and feature diversity can achieve better generalizability. Based on this observation, we propose a novel generalizable framework (MitDet) for mitosis detection. The data diversity is considered by the proposed diversity-guided sample balancing (DGSB). And the feature diversity is preserved by inter- and intra- class feature diversity-preserved module (InCDP). Stain enhancement (SE) module is introduced to enhance the domain-relevant diversity of both data and features simultaneously. Extensive experiments have demonstrated that our proposed model outperforms all the SOTA approaches in several popular mitosis detection datasets in both internal and external test sets using minimal annotation efforts with point annotations only. Comprehensive ablation studies have also proven the effectiveness of the rethinking of data and feature diversity balancing. By analyzing the results quantitatively and qualitatively, we believe that our proposed model not only achieves SOTA performance but also might inspire the future studies in new perspectives. Source code is at https://github.com/Onehour0108/MitDet.

翻译：有丝分裂检测是计算病理学中的基础任务之一，由于有丝分裂细胞的异质性，该任务极具挑战性。现有研究大多通过增加模型复杂度来从技术层面解决异质性问题，但缺乏对生物学知识的考量以及复杂的模型设计可能导致过拟合问题，同时限制检测模型的泛化能力。本文系统研究了不同有丝分裂阶段的形态学表现及模糊的非有丝分裂细胞，发现平衡数据与特征的多样性能够实现更好的泛化性能。基于这一发现，我们提出了一种新型可泛化框架（MitDet）用于有丝分裂检测。通过提出的多样性引导样本平衡方法（DGSB）考虑了数据多样性，通过类间与类内特征多样性保持模块（InCDP）保留了特征多样性。引入染色增强模块（SE）以同步增强数据与特征的域相关多样性。大量实验表明，在多个主流有丝分裂检测数据集的内外部测试集上，所提模型仅需最少的点标注标注努力即可超越所有现有最优方法。全面的消融研究也证明了重新思考数据与特征多样性平衡的有效性。通过定量与定性结果分析，我们相信所提模型不仅实现了最优性能，还可能从新视角启发未来研究。源代码见https://github.com/Onehour0108/MitDet。