Liu Weihuang, Juhas Mario, Zhang Yang
College of Science, Harbin Institute of Technology, Shenzhen, China.
Faculty of Science and Medicine, University of Fribourg, Fribourg, Switzerland.
Front Genet. 2020 Sep 4;11:547327. doi: 10.3389/fgene.2020.547327. eCollection 2020.
Classification of histopathological images of cancer is challenging even for well-trained professionals, due to the fine-grained variability of the disease. Deep Convolutional Neural Networks (CNNs) showed great potential for classification of a number of the highly variable fine-grained objects. In this study, we introduce a Bilinear Convolutional Neural Networks (BCNNs) based deep learning method for fine-grained classification of breast cancer histopathological images. We evaluated our model by comparison with several deep learning algorithms for fine-grained classification. We used bilinear pooling to aggregate a large number of orderless features without taking into consideration the disease location. The experimental results on BreaKHis, a publicly available breast cancer dataset, showed that our method is highly accurate with 99.24% and 95.95% accuracy in binary and in fine-grained classification, respectively.
即使对于训练有素的专业人员来说，癌症组织病理学图像的分类也具有挑战性，因为这种疾病存在细微的差异。深度卷积神经网络（CNN）在对许多高度可变的细粒度对象进行分类方面显示出巨大潜力。在本研究中，我们引入了一种基于双线性卷积神经网络（BCNN）的深度学习方法，用于乳腺癌组织病理学图像的细粒度分类。我们通过与几种用于细粒度分类的深度学习算法进行比较来评估我们的模型。我们使用双线性池化来聚合大量无序特征，而不考虑疾病位置。在公开可用的乳腺癌数据集BreaKHis上的实验结果表明，我们的方法具有很高的准确性，在二元分类和细粒度分类中的准确率分别为99.24%和95.95%。
