MuDeRN: Multi-category classification of breast histopathological image using deep residual networks
MuDeRN: Multi-category classification of breast histopathological image using deep residual networks
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DOI：
10.1016/j.artmed.2018.04.005
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发表时间：
2018-06-01
影响因子：
7.5
通讯作者：
Mello-Thoms, Claudia
中科院分区：
文献类型：
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作者：
Gandomkar, Ziba;Brennan, Patrick C.;Mello-Thoms, Claudia
动机：确定癌亚型有助于选择合适的治疗方案，确定良性病变的亚型有助于估计患者未来发生癌症的风险。病理学家对病变亚型的评估被认为是金标准，然而，有时病理学家之间对病变亚型区分的强烈分歧已在文献中报道过。目的：建立苏木精-伊红染色乳腺数字切片的良性与癌性分类框架，并将癌性与良性病例分别分为4个不同的亚型。材料和方法：我们使用了来自公共数据库（BreakHis）的81例患者的数据，其中每个患者都有四种放大倍数（x40, x100， x200和x400）的图像，总共7786张图像。所提出的框架，称为MuDeRN（使用深度残差网络的乳腺组织病理图像的多类别分类），包括两个阶段。在第一阶段，针对每个放大系数，训练一个152层的深度残差网络（ResNet），用于将图像中的斑块分类为良性或恶性。下一阶段，将分类为恶性的图像再细分为4个癌亚类，将分类为良性的图像再细分为4个亚型。最后，使用元决策树将ResNets在不同放大倍数下处理的图像输出结合起来，对每个患者进行诊断。结果：对于图像的良恶性分类，在x40、x100、x200、x400倍率下，MuDeRN第一阶段的正确分类率分别为98.52%、97.90%、98.33%、97.66%。基于MuDeRN两个阶段的输出对图像进行八类分类，四种放大倍数下的ccr分别为95.40%、94.90%、95.70%和94.60%。最后，对于患者层面的诊断，MuDeRN对8类分类的CCR达到96.25%。结论：momern有助于乳腺病变的分类。(C) 2018 Elsevier B.V.版权所有
Motivation: Identifying carcinoma subtype can help to select appropriate treatment options and determining the subtype of benign lesions can be beneficial to estimate the patients' risk of developing cancer in the future. Pathologists' assessment of lesion subtypes is considered as the gold standard, however, sometimes strong disagreements among pathologists for distinction among lesion subtypes have been previously reported in the literature.Objective: To propose a framework for classifying hematoxylin-eosin stained breast digital slides either as benign or cancer, and then categorizing cancer and benign cases into four different subtypes each.Materials and methods: We used data from a publicly available database (BreakHis) of 81 patients where each patient had images at four magnification factors (x40, x100, x200, and x400) available, for a total of 7786 images. The proposed framework, called MuDeRN (Multi-category classification of breast histopathological image using DEep Residual Networks) consisted of two stages. In the first stage, for each magnification factor, a deep residual network (ResNet) with 152 layers has been trained for classifying patches from the images as benign or malignant. In the next stage, the images classified as malignant were subdivided into four cancer subcategories and those categorized as benign were classified into four subtypes. Finally, the diagnosis for each patient was made by combining outputs of ResNets' processed images in different magnification factors using a meta-decision tree.Results: For the malignant/benign classification of images, MuDeRN's first stage achieved correct classification rates (CCR) of 98.52%, 97.90%, 98.33%, and 97.66% in x40, x100, x200, and x400 magnification factors respectively. For eight-class categorization of images based on the output of MuDeRN's both stages, CCRs in four magnification factors were 95.40%, 94.90%, 95.70%, and 94.60%. Finally, for making patient-level diagnosis, MuDeRN achieved a CCR of 96.25% for eight-class categorization.Conclusions: MuDeRN can be helpful in the categorization of breast lesions. (C) 2018 Elsevier B.V. All rights reserved.