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MaskMitosis: a deep learning framework for fully supervised, weakly supervised, and unsupervised mitosis detection in histopathology images.
Medical & Biological Engineering & Computing ( IF 3.2 ) Pub Date : 2020-05-22 , DOI: 10.1007/s11517-020-02175-z
Meriem Sebai 1 , Xinggang Wang 2 , Tianjiang Wang 1
Affiliation  

Counting the mitotic cells in histopathological cancerous tissue areas is the most relevant indicator of tumor grade in aggressive breast cancer diagnosis. In this paper, we propose a robust and accurate technique for the automatic detection of mitoses from histological breast cancer slides using the multi-task deep learning framework for object detection and instance segmentation Mask RCNN. Our mitosis detection and instance segmentation framework is deployed for two main tasks: it is used as a detection network to perform mitosis localization and classification in the fully annotated mitosis datasets (i.e., the pixel-level annotated datasets), and it is used as a segmentation network to estimate the mitosis mask labels for the weakly annotated mitosis datasets (i.e., the datasets with centroid-pixel labels only). We evaluate our approach on the fully annotated 2012 ICPR grand challenge dataset and the weakly annotated 2014 ICPR MITOS-ATYPIA challenge dataset. Our evaluation experiments show that we can obtain the highest F-score of 0.863 on the 2012 ICPR dataset by applying the mitosis detection and instance segmentation model trained on the pixel-level labels provided by this dataset. For the weakly annotated 2014 ICPR dataset, we first employ the mitosis detection and instance segmentation model trained on the fully annotated 2012 ICPR dataset to segment the centroid-pixel annotated mitosis ground truths, and produce the mitosis mask and bounding box labels. These estimated labels are then used to train another mitosis detection and instance segmentation model for mitosis detection on the 2014 ICPR dataset. By adopting this two-stage framework, our method outperforms all state-of-the-art mitosis detection approaches on the 2014 ICPR dataset by achieving an F-score of 0.475. Moreover, we show that the proposed framework can also perform unsupervised mitosis detection through the estimation of pseudo labels for an unlabeled dataset and it can achieve promising detection results. Code has been made available at: https://github.com/MeriemSebai/MaskMitosis. Graphical Abstract Overview of MaskMitosis framework.

中文翻译:

MaskMitosis:一种深度学习框架,用于组织病理学图像中完全监督,弱监督和无监督的有丝分裂检测。

计数组织病理学癌变组织区域中的有丝分裂细胞是侵袭性乳腺癌诊断中最相关的肿瘤等级指标。在本文中,我们提出了一种稳健而准确的技术,该技术使用多任务深度学习框架进行对象检测和实例分割Mask RCNN,从组织学乳腺癌切片中自动检测有丝分裂。我们的有丝分裂检测和实例分割框架部署了两个主要任务:它用作检测网络,以在完全注释的有丝分裂数据集(即像素级带注释的数据集)中执行有丝分裂的定位和分类,并且用作分割网络,以评估弱注释的有丝分裂数据集(即仅具有质心像素标签的数据集)的有丝分裂蒙版标签。我们对完全注释的2012 ICPR大挑战数据集和弱注释的2014 ICPR MITOS-ATYPIA挑战数据集评估我们的方法。我们的评估实验表明,通过应用在该数据集提供的像素级标签上训练的有丝分裂检测和实例分割模型,我们可以获得2012 ICPR数据集上的最高F分数0.863。对于弱注释的2014 ICPR数据集,我们首先采用在完全注释的2012 ICPR数据集上训练的有丝分裂检测和实例分割模型来分割质心像素注释的有丝分裂基础真相,并生成有丝分裂蒙版和边界框标签。这些估计的标签然后用于训练另一个有丝分裂检测和实例分割模型,以在2014 ICPR数据集上进行有丝分裂检测。通过采用此两阶段框架,我们的方法的F得分为0.475,优于2014 ICPR数据集上所有最新的有丝分裂检测方法。此外,我们表明,提出的框架还可以通过估计未标记数据集的伪标记来执行无监督有丝分裂检测,并且可以实现有希望的检测结果。代码已在以下位置提供:https://github.com/MeriemSebai/MaskMitosis。MaskMitosis框架的图形摘要。com / MeriemSebai / MaskMitosis。MaskMitosis框架的图形摘要。com / MeriemSebai / MaskMitosis。MaskMitosis框架的图形摘要。
更新日期:2020-05-22
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