Mitotic count is an important indicator for assessing the invasiveness of breast cancers. Currently, the number of mitoses is manually counted by pathologists, which is both tedious and time-consuming. To address this situation, we propose a fast and accurate method to automatically detect mitosis from the histopathological images. The proposed method can automatically identify mitotic candidates from histological sections for mitosis screening. Specifically, our method exploits deep convolutional neural networks to extract high-level features of mitosis to detect mitotic candidates. Then, we use spatial attention modules to re-encode mitotic features, which allows the model to learn more efficient features. Finally, we use multi-branch classification subnets to screen the mitosis. Compared to existing related methods in literature, our method obtains the best detection results on the dataset of the International Pattern Recognition Conference (ICPR) 2012 Mitosis Detection Competition. Code has been made available at: https://github.com/liushaomin/MitosisDetection.

中文翻译：

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用于从组织病理学图像中检测有丝分裂的注意力引导多分支卷积神经网络

有丝分裂计数是评估乳腺癌侵袭性的重要指标。目前，有丝分裂的数量由病理学家手动计算，既繁琐又耗时。为了解决这种情况，我们提出了一种快速准确的方法来从组织病理学图像中自动检测有丝分裂。所提出的方法可以从组织切片中自动识别有丝分裂候选者以进行有丝分裂筛选。具体来说，我们的方法利用深度卷积神经网络来提取有丝分裂的高级特征来检测有丝分裂候选者。然后，我们使用空间注意力模块重新编码有丝分裂特征，这使模型能够学习更有效的特征。最后，我们使用多分支分类子网来筛选有丝分裂。与文献中已有的相关方法相比，我们的方法在国际模式识别会议 (ICPR) 2012 有丝分裂检测竞赛的数据集上获得了最佳检测结果。代码已在以下位置提供：https://github.com/liushaomin/MitosisDetection。