Classification of breast cancer histology images using Convolutional Neural Networks.
Classification of breast cancer histology images using Convolutional Neural Networks.
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DOI：
10.1371/journal.pone.0177544
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Campilho A
中科院分区：
文献类型：
--
作者：
Araújo T;Aresta G;Castro E;Rouco J;Aguiar P;Eloy C;Polónia A;Campilho A
乳腺癌是全球癌症死亡的主要原因之一。苏木精-伊红染色的活检组织的诊断不是微不足道的，专家们经常对最终诊断意见不一。计算机辅助诊断系统有助于降低这一过程的成本和提高效率。传统的分类方法依赖于基于领域知识的针对特定问题的特征提取方法。为了克服基于特征的方法的诸多困难，深度学习方法正成为重要的替代方法。提出了一种利用卷积神经网络对苏木精-伊红染色乳腺活检图像进行分类的方法。图像分为正常组织、良性病变、原位癌和浸润性癌四类，并分为癌和非癌两类。该网络的架构被设计成在不同的尺度上检索信息，包括细胞核和整个组织结构。这种设计允许将所提出的系统扩展到全幻灯片组织学图像。由CNN提取的特征也被用于训练支持向量机分类器。四类准确率为77.8%，癌/非癌准确率为83.3%。我们的方法对癌症的敏感性为95.6%。
Breast cancer is one of the main causes of cancer death worldwide. The diagnosis of biopsy tissue with hematoxylin and eosin stained images is non-trivial and specialists often disagree on the final diagnosis. Computer-aided Diagnosis systems contribute to reduce the cost and increase the efficiency of this process. Conventional classification approaches rely on feature extraction methods designed for a specific problem based on field-knowledge. To overcome the many difficulties of the feature-based approaches, deep learning methods are becoming important alternatives. A method for the classification of hematoxylin and eosin stained breast biopsy images using Convolutional Neural Networks (CNNs) is proposed. Images are classified in four classes, normal tissue, benign lesion, in situ carcinoma and invasive carcinoma, and in two classes, carcinoma and non-carcinoma. The architecture of the network is designed to retrieve information at different scales, including both nuclei and overall tissue organization. This design allows the extension of the proposed system to whole-slide histology images. The features extracted by the CNN are also used for training a Support Vector Machine classifier. Accuracies of 77.8% for four class and 83.3% for carcinoma/non-carcinoma are achieved. The sensitivity of our method for cancer cases is 95.6%.