关键词: 乳腺癌, 前哨淋巴结转移, PCam, 多路卷积网络, SENet

Abstract:

Metastasis of breast cancer cells is an important factor affecting the prognosis of patients. The traditional pathologist’s examination process is redundant and time-consuming and easy to miss the micrometastases. At present, there have been achievements in the study of sentinel lymph node metastasis of breast cancer using convolutional neural network, but the accuracy is not high and the detection effect of micrometastasis is not good. Based on the breast cancer sentinel lymph node pathological image data set（PCam）, this paper designs and proposes a SENet multi-channel convolutional neural network model, which uses stacked multi-channel convolutional units and SENet modules, skipping cross-layer connections, standard convolution and depthwise separable convolution fusion, addition and concatenation operations. The model weight is obtained by using 50% image iteration training for 35 times, and then the accuracy and AUC value indexes are used to test the test image. The accuracy is 97.32% and the AUC value is 98.05%. Compared with the existing research results and the mainstream convolutional network model, the AUC value of this model ranks the first in the case of 49%, 51% and 100% test sets. The results show that this model has a high accuracy in the detection of lymph node metastasis, and a good detection performance for micrometastasis.

Key words: breast cancer, sentinel lymph node metastasis, PCam, multi-channel convolutional network, SENet