BDCN: Semantic Embedding Self-explanatory Breast Diagnostic Capsules Network

BDCN: Semantic Embedding Self-explanatory Breast Diagnostic Capsules Network Dehua Chen, keting Zhong, Jianrong He* School of Computer Science and Engineering, Donghua University, Shanghai, China Comprehensive Breast Health Center, Shanghai Ruijin Hospital, Shanghai, China chendehua@dhu.edu.cn {katyzhong7,hejrongbreastsg}@163.com Abstract Building an interpretable AI diagnosis system for breast cancer is an important embodiment of AI assisted medicine. Traditional breast cancer diagnosis methods based on machine learning are easy to explain, but the accuracy is very low. Deep neural network greatly improves the accuracy of diagnosis, but the black box model does not provide transparency and interpretation. In this work, we propose a semantic embedding self-explanatory Breast Diagnostic Capsules Network (BDCN). This model is the first to combine the capsule network with semantic embedding for the AI diagnosis of breast tumors, using capsules to simulate semantics. We pre-trained the extrac- tion word vector by embedding the semantic tree into the BERT and used the capsule network to improve the semantic representation of multiple heads of attention to construct the extraction feature, the capsule network was extended from the computer vision classification task to the text classification task. Simultaneously, both the back propagation principle and dynamic routing algorithm are used to realize the local interpretability of the diagnostic model. The experimen- tal results show that this breast diagnosis model improves the model performance and has good interpretability, which is more suitable for clinical situations. 1 Introduction Breast cancer is an important killer threatening women’s health because of rising incidence. Early detection and diagnosis are the key to reduce the mortality rate of breast cancer and improve the quality of life of patients. Mammary gland molybdenum target report contains rich semantic information, which can directly reflect the results of breast cancer screening (CACA-CBCS, 2019), and AI-assisted diagno- sis of breast cancer is an important means. Therefore, various diagnostic models were born. Mengwan (2020) used support vector machine(SVM) and Naive Bayes to classify morphological features with an accuracy of 91.11%. Wei (2009) proposed a classification method of breast cancer based on SVM, and the accuracy of the classifier experiment is 79.25%. These traditional AI diagnoses of breast tumors have limited data volume and low accuracy. Deep Neural Networks (DNN) enters into the ranks of the diagno- sis of breast tumor. Wang (2019) put forward a kind of based on feature fusion with CNN deep features of breast computer-aided diagnosis methods, the accuracy is 92.3%. Zhao (2018) investigated capsule networks with dynamic routing for text classification, which proves the feasibility of text categorization. Existing models have poor predictive effect and lack of interpretation, which can not meet the clinical needs. Based on the above pain points, we propose a semantic embedding self-explanatory Breast Diagnostic Capsules Network (BDCN), which diagnoses breast tumors based on the mammary gland molybdenum target report. Our contributions are as follows: • Semantic segmentation algorithm is used to segment breast cancer lesions. • Semantic tree is integrated into Bidirectional Encoder Representation from Transformers(BERT) pre-training to obtain word vectors. ©2021 China National Conference on Computational Linguistics Published …

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