Abstract
Counting mitotic figures is time-intensive for pathologists and leads to inter-observer variability. Artificial intelligence (AI) promises a solution by automatically detecting mitotic figures while maintaining decision consistency. However, AI tools are susceptible to domain shift, where a significant drop in performance can occur due to differences in the training and testing sets, including morphological diversity between organs, species, and variations in staining protocols. Furthermore, the number of mitoses is much less than the count of normal nuclei, which introduces severely imbalanced data for the detection task. In this work, we formulate mitosis detection as a pixel-level segmentation and propose a teacher-student model that simultaneously addresses mitosis detection (Track 1) and atypical mitosis classification (Track 2). Our method is based on a UNet segmentation backbone that integrates domain generalization modules, namely contrastive representation learning and domain-adversarial training. A teacher-student strategy is employed to generate pixel-level pseudo-masks not only for annotated mitoses and hard negatives but also for normal nuclei, thereby enhancing feature discrimination and improving robustness against domain shift. For the classification task, we introduce a multi-scale CNN classifier that leverages feature maps from the segmentation model within a multi-task learning paradigm. On the preliminary test set, the algorithm achieved an F1 score of 0.7660 in Track 1 and balanced accuracy of 0.8414 in Track 2, demonstrating the effectiveness of integrating segmentation-based detection and classification into a unified framework for robust mitosis analysis.
Abstract (translated)
病理学家统计有丝分裂图像是耗时的任务，并且会导致观察者之间的一致性问题。人工智能（AI）通过自动检测有丝分裂并保持决策一致性，提供了一种解决方案。然而，AI工具容易受到领域偏移(domain shift)的影响，在训练集和测试集中存在形态多样性的差异、物种的不同以及染色协议的变化时，性能可能会显著下降。此外，有丝分裂的数量远少于正常细胞核的数量，这使得检测任务面临严重的数据不平衡问题。 在这项工作中，我们将有丝分裂的检测视为像素级分割，并提出了一种师生模型，该模型同时解决有丝分裂的检测（Track 1）和非典型有丝分裂分类（Track 2）。我们的方法基于一个集成领域泛化模块的UNet分割骨干网络，这些模块包括对比表示学习和域对抗训练。采用教师-学生策略生成像素级伪掩模，不仅用于注释的有丝分裂和难例，还用于正常细胞核，从而增强特征区分度并提高对领域偏移的鲁棒性。 对于分类任务，我们引入了一个多尺度CNN分类器，在一个多任务学习框架中利用来自分割模型的功能图。在初步测试集上，该算法在Track 1中的F1分数为0.7660，并且在Track 2中的平衡准确率为0.8414，证明了将基于分割的检测和分类整合到统一框架中进行稳健有丝分裂分析的有效性。
URL
https://arxiv.org/abs/2509.03614
