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史文旭. 改进卷积神经网络在腺癌病理图像分类中的应用[J]. 科学技术与工程, 2019, 19(35): 279-285.
shiwenxu.Application of improved convolution neural network in Pathological Image Classification[J].Science Technology and Engineering,2019,19(35):279-285.
改进卷积神经网络在腺癌病理图像分类中的应用
Application of improved convolution neural network in Pathological Image Classification
投稿时间:2019-05-06  修订日期:2019-08-31
DOI:
中文关键词:  反卷积 Inception结构 特征融合 病理图象 CNN
英文关键词:deconvolution inception structure feature fusion pathological Image cnn
基金项目:四川省科技厅重点研发项目(2018SZ0040)和四川省新一代人工智能重大专项(2018GZDZX0036)
  
作者单位
史文旭 中国科学院成都计算机应用研究所院
中国科学院大学
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中文摘要:
      针对传统卷积神经网络(CNN)稀疏的网络结构无法保留全连接网络密集计算的高效性和在实验过程中卷积特征利用率低造成的分类结果不准确或收敛速度较慢的问题,提出了一种基于CNN的多尺度方法结合反卷积网络的特征提取算法(MSDCNN)并对腺癌病理图像进行分类。首先,利用反卷积操作实现不同尺度特征的融合,然后利用Inception结构不同尺度卷积核提取多尺度特征,最后通过Softmax方法对图像进行分类。在腺癌病理细胞图像进行的分类实验结果表明,在最后的卷积特征尺度相同的情况下,MSDCNN算法比传统的CNN算法分类精度提高了约14%,比同样基于多尺度特征的融合网络模型方法分类精度提高了约1.2%。
英文摘要:
      The sparse structure for traditional Convolutional Neural Network (CNN) can not preserve the high efficiency of the dense network-intensive computing and the low utilization of convolution features in the experimental process,which leads to inaccurate classification results and slow convergence rate. To solve above problems, a method of extracting image features that multi-scale method combined with a deconvolution network algorithm (MSDCNN) based on CNN was proposed, and applied in the glandular images classification. Firstly, the deconvolution operation was used to achieve the fusion of different scale features, then the multi-scales feature was extracted by using different scale convolution kernels of the Inception structure. Finally, the image was classified by Softmax method. Experiments were conducted on the pathological cell images of glandular, and the experimental results showed that, the accuracy of MSDCNN algorithm is about 14% higher than that of traditional CNN algorithm under the same feature scale as the last layer, and the accuracy of MSDCNN algorithm is about 1.2% higher than that of fusion network model based on multi-scale features.
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