Counting of mitotic figures is a fundamental step in grading and prognostication of several cancers. However, manual mitosis counting is tedious and time-consuming. In addition, variation in the appearance of mitotic figures causes a high degree of discordance among pathologists. With advances in deep learning models, several automatic mitosis detection algorithms have been proposed but they are sensitive to {\em domain shift} often seen in histology images. We propose a robust and efficient two-stage mitosis detection framework, which comprises mitosis candidate segmentation ({\em Detecting Fast}) and candidate refinement ({\em Detecting Slow}) stages. The proposed candidate segmentation model, termed \textit{EUNet}, is fast and accurate due to its architectural design. EUNet can precisely segment candidates at a lower resolution to considerably speed up candidate detection. Candidates are then refined using a deeper classifier network, EfficientNet-B7, in the second stage. We make sure both stages are robust against domain shift by incorporating domain generalization methods. We demonstrate state-of-the-art performance and generalizability of the proposed model on the three largest publicly available mitosis datasets, winning the two mitosis domain generalization challenge contests (MIDOG21 and MIDOG22). Finally, we showcase the utility of the proposed algorithm by processing the TCGA breast cancer cohort (1,125 whole-slide images) to generate and release a repository of more than 620K mitotic figures.

翻译：有丝分裂像计数是多种癌症分级和预后判断的基本步骤。然而，手动有丝分裂计数既繁琐又耗时。此外，有丝分裂像外观的变异性导致病理学家之间存在高度不一致性。随着深度学习模型的进步，已提出多种自动有丝分裂检测算法，但这些算法对组织学图像中常见的**领域偏移**较为敏感。本文提出一种稳健高效的两阶段有丝分裂检测框架，包括有丝分裂候选分割（快速检测）和候选细化（慢速检测）阶段。所提出的候选分割模型名为EUNet，因其架构设计而具有快速准确的特点。EUNet能够在较低分辨率下精确分割候选区域，从而显著加快候选检测速度。随后，在第二阶段使用更深的分类器网络EfficientNet-B7对候选区域进行细化。我们通过引入领域泛化方法确保两个阶段均对领域偏移具有稳健性。我们在三个最大的公开有丝分裂数据集上展示了所提模型的最先进性能和泛化能力，并赢得了两项有丝分裂领域泛化挑战赛（MIDOG21和MIDOG22）。最后，我们通过处理TCGA乳腺癌队列（1125张全切片图像）生成并发布了包含超过62万个有丝分裂像的数据资源库，展示了所提算法的实用性。