• 人工智能 •

### 淋巴结转移检测的八度卷积方法

1. 长沙理工大学 电气与信息工程学院, 长沙 410114
• 收稿日期:2019-07-29 修回日期:2019-10-25 出版日期:2020-03-10 发布日期:2019-11-07
• 通讯作者: 魏哲
• 作者简介:魏哲(1992-),男,宁夏银川人,硕士研究生,CCF会员,主要研究方向:机器学习、自然语言处理;王小华(1968-),男,湖南长沙人,教授,博士,主要研究方向:智能图像处理、智能信号处理。
• 基金资助:
长株潭重点专项。

### Octave convolution method for lymph node metastases detection

1. School of Electrical and Information Engineering, Changsha University of Science&Technology, Changsha Hunan 410114, China
• Received:2019-07-29 Revised:2019-10-25 Online:2020-03-10 Published:2019-11-07
• Supported by:
This work is partially supported by the Special Project of Chang-Zhu-Tan City Cluster.

Abstract: Focused on the problems of low accuracy and long time cost of manual detection of breast cancer lymph node metastasis, a neural network detection model based on residual network structure and with Octave convolution method to design convolution layers was proposed. Firstly, based on the convolution layer of residual network, the input and output eigenvectors in the convolution layer were divided into high frequencies and low frequencies, and the channel width and height of the low-frequencies were reduced to half of those of the high frequencies. Then, the convolution operation between the low-frequency vector and the high-frequency vector was realized by up-sampling the low-frequency vector with the reduction by half, and the convolution operation between the high-frequency vector and the low-frequency vector was realized by average pooling of the high-frequency vector. Finally, the convolutions between high-frequency vectors and between high-frequency vector and low-frequency vector were added to obtain the high-frequency output, and the convolutions between low-frequency vectors and between low-frequency vector and high-frequency vector were added to obtain the low-frequency output. In this way, Octave convolution layer was constructed, and all convolution layers in residual network were replaced by Octave convolution layers to construct the detection model. In theory, the amount of computation of convolution in Octave convolution layer was reduced by 75%, effectively speeding up the training of the model. On the cloud server with maximum memory of 13 GB and free disk size of 4.9 GB, the PCam (PatchCamelyon) dataset was used for testing. The results show that the model has the recognition accuracy of 95.1%, the memory occupied of 8.7 GB, the disk occupied of 356.4 MB, and the average single training time of 4 minutes 42 seconds. Compared with the ResNet50, this model has the accuracy reduced by 0.6%, the memory saved by 0.6 GB, the disk saved by 105.9 MB, and the single training time shortened by 1 minute. The experimental results demonstrate that the proposed model has high recognition accuracy, short training time and small memory consumption, which reduces the requirement of computing resources under the background of big data era, making the model have application value.