Classification of breast cancer histopathological images using interleaved DenseNet with SENet (IDSNet)
Classification of breast cancer histopathological images using interleaved DenseNet with SENet (IDSNet)
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DOI：
10.1371/journal.pone.0232127
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发表时间：
2020-05-04
期刊：
影响因子：
3.7
通讯作者：
Li, Tie-Qiang
中科院分区：
文献类型：
--
作者：
Li, Xia;Shen, Xi;Li, Tie-Qiang
在这项研究中，我们提出了一种新的卷积神经网络（CNN）架构，用于组织学图像中良性和恶性乳腺癌（BC）的分类。为了改进特征信息的交付和使用，我们选择DenseNet作为基本构建块，并将其与挤压和激励（SENet）模块交织在一起。我们通过使用公共领域的BreakHis数据集对所提出的框架进行了广泛的实验，并证明与文献中报道的最先进的CNN方法相比，所提出的框架可以显着提高BC分类的准确性。
In this study, we proposed a novel convolutional neural network (CNN) architecture for classification of benign and malignant breast cancer (BC) in histological images. To improve the delivery and use of feature information, we chose the DenseNet as the basic building block and interleaved it with the squeeze-and-excitation (SENet) module. We conducted extensive experiments with the proposed framework by using the public domain BreakHis dataset and demonstrated that the proposed framework can produce significantly improved accuracy in BC classification, compared with the state-of-the-art CNN methods reported in the literature.