Mitotic figure count is an important marker of tumor proliferation and has
been shown to be associated with patients' prognosis. Deep learning based
mitotic figure detection methods have been utilized to automatically locate the
cell in mitosis using hematoxylin \& eosin (H\&E) stained images. However, the
model performance deteriorates due to the large variation of color tone and
intensity in H\&E images. In this work, we proposed a two stage mitotic figure
detection framework by fusing a detector and a deep ensemble classification
model. To alleviate the impact of color variation in H\&E images, we utilize
both stain normalization and data augmentation, aiding model to learn color
irrelevant features. The proposed model obtains an F1 score of 0.7550 on the
preliminary testing set released by the MIDOG challenge.

本文提出了一种两阶段有深度集成分类模型的有机物卷积神经网络（CNN）检测框架，以便于减轻图像色彩变化对细胞检测的影响，并使用标准化和数据增广来辅助模型训练，旨在自动定位有丝分裂的细胞。该模型在 MIDOG 挑战初步测试集中获得了 0.7550 的 F1 得分。