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首页> 外文会议>International Conference on Signal Image Processing and Communication >Densely Connected Convolutional Networks for Breast Cancer Histopathological Image Classification
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Densely Connected Convolutional Networks for Breast Cancer Histopathological Image Classification

机译：密集连接的乳腺癌组织病理学图像分类卷积网络

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The computer-assisted classification of breast cancer histopathological image in the future is an essential method for the improvement of the diagnostic performance, thus reducing breast cancer deaths. Although deep convolutional neural networks (DCNNs) have made dramatic breakthroughs, many image classification tasks still remain challenging due to the insufficiency of training data and the lack of the ability to focus on improving classification efficiency. To address these issues, we share a densely connected convolutional network (DenseNet) model that is composed of 3 layers DenseNet, pooling layer, and a classification layer for breast cancer classification in microscopic images. Each layer of DenseNet contains 4 dense blocks. Each dense block jointly uses the dense connection and a novel attention learning mechanisms to increase its ability for discriminative representation. Meanwhile, the transfer learning algorithm is applied to determine the model parameters to extract the features of the patient image that is performed. In order to ensure sufficient data volume, a data enhancement method based on the quad-tree principle is proposed for high-resolution images. On the other hand, the classification probability of each part after dicing is fused by three algorithms of addition, product, and maximum. We evaluated our DenseNet model on the BreastKHis dataset. Our results indicate that the DenseNet model and data enhancement method we adopted can adaptively focus on the study of breast cancer histopathological image classification, thus achieving the state-of-the-art performance in breast cancer classification. The results of the experiments are in terms of patient-level and image-level accuracy. The best recognition accuracy increased to 90.9%-92.5% and 89.3%-91.8%, respectively, compared with previous studies.
机译：未来计算机辅助分类乳腺癌组织病理学形象是改善诊断性能的基本方法，从而减少乳腺癌死亡。虽然深度卷积神经网络（DCNNS）进行了戏剧性的突破，但由于培训数据的不足以及缺乏专注于提高分类效率的能力，许多图像分类任务仍然挑战。为了解决这些问题，我们共享一个密集的连接卷积网络（DENSENET）模型，该网络由3层DENENET，汇集层和微观图像中乳腺癌分类的分类层组成。每层Densenet包含4个密集块。每个密集的块共同使用密集的连接和新的注意力学习机制来提高其辨别表现的能力。同时，应用转移学习算法来确定提取执行的患者图像的特征的模型参数。为了确保足够的数据量，提出了一种基于四树原理的数据增强方法，用于高分辨率图像。另一方面，切割后每个部分的分类概率由另外的添加，产品和最大算法融合。我们在Bastickhis DataSet上评估了DENSenet模型。我们的结果表明，我们采用的DenSenet模型和数据增强方法可以自适应地关注乳腺癌组织病理学图像分类的研究，从而实现乳腺癌分类中的最新性能。实验结果符合患者水平和图像水平精度。与以前的研究相比，最佳识别准确性分别增加到90.9％-92.5％和89.3％-91.8％。

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