Breast cancer is one of the main causes of cancer death worldwide. Early
diagnostics significantly increases the chances of correct treatment and
survival, but this process is tedious and often leads to a disagreement between
pathologists. Computer-aided diagnosis systems showed potential for improving
the diagnostic accuracy. In this work, we develop the computational approach
based on deep convolution neural networks for breast cancer histology image
classification. Hematoxylin and eosin stained breast histology microscopy image
dataset is provided as a part of the ICIAR 2018 Grand Challenge on Breast
Cancer Histology Images. Our approach utilizes several deep neural network
architectures and gradient boosted trees classifier. For 4-class classification
task, we report 87.2% accuracy. For 2-class classification task to detect
carcinomas we report 93.8% accuracy, AUC 97.3%, and sensitivity/specificity
96.5/88.0% at the high-sensitivity operating point. To our knowledge, this
approach outperforms other common methods in automated histopathological image
classification. The source code for our approach is made publicly available at
this https URL

本研究基于深度卷积神经网络开发了计算方法，用于乳腺癌组织病理图像分类，包括四类和两类（用于检测癌变）分类任务，在高灵敏度操作点上，我们报告了 93.8% 的准确性，97.3% 的 AUC 和 96.5/88.0% 的敏感性 / 特异性，超过了其他常见的自动组织病理学图像分类方法。