有丝分裂计数（MC）是癌症诊断和分级的重要组织学参数，但从全幅组织病理学图像获取MC的手动过程非常耗时且易于出错。因此，人们提出了深度学习模型来促进这一过程。现有的方法利用两阶段流程：检测阶段用于识别潜在有丝分裂细胞的位置，分类阶段用于改善预测置信度。然而，由于检测阶段的预测质量较差以及两阶段训练数据分布不匹配，这种流程可以导致分类阶段的不一致性。在这项研究中，我们提出了一种改进的深度学习流程Refine Cascade Network（ReCasNet），可以通过三个改进来缓解上述问题。首先，采用窗口重新定位方法来减少在检测阶段生成的质量差的误报。其次，使用另一个深度学习模型进行对象重新裁剪，以调整不合适数对象。第三，引入改进的数据选择策略，以减少分类阶段中的训练数据分布不匹配。ReCasNet在两个大规模有丝分裂细胞识别数据集上进行了评估，分别为犬皮肤肥大细胞瘤（CCMCT）和犬乳腺癌（CMC），结果在有丝分裂细胞检测的F1分数中获得了高达4.8％的百分点改进，MC预测的平均绝对百分比误差（MAPE）减少44.1％。ReCasNet所基于的技术可以推广到其他两阶段目标检测流程，并有助于改善广泛的数字病理学应用中深度学习模型的性能。版权所有© 2022 Elsevier B.V.保留所有权利。

Mitotic count (MC) is an important histological parameter for cancer diagnosis and grading, but the manual process for obtaining MC from whole-slide histopathological images is very time-consuming and prone to error. Therefore, deep learning models have been proposed to facilitate this process. Existing approaches utilize a two-stage pipeline: the detection stage for identifying the locations of potential mitotic cells and the classification stage for refining prediction confidences. However, this pipeline formulation can lead to inconsistencies in the classification stage due to the poor prediction quality of the detection stage and the mismatches in training data distributions between the two stages. In this study, we propose a Refine Cascade Network (ReCasNet), an enhanced deep learning pipeline that mitigates the aforementioned problems with three improvements. First, window relocation was used to reduce the number of poor quality false positives generated during the detection stage. Second, object re-cropping was performed with another deep learning model to adjust poorly centered objects. Third, improved data selection strategies were introduced during the classification stage to reduce the mismatches in training data distributions. ReCasNet was evaluated on two large-scale mitotic figure recognition datasets, canine cutaneous mast cell tumor (CCMCT) and canine mammary carcinoma (CMC), which resulted in up to 4.8% percentage point improvements in the F1 scores for mitotic cell detection and 44.1% reductions in mean absolute percentage error (MAPE) for MC prediction. Techniques that underlie ReCasNet can be generalized to other two-stage object detection pipeline and should contribute to improving the performances of deep learning models in broad digital pathology applications.Copyright © 2022 Elsevier B.V. All rights reserved.