BDCN : Semantic Embedding Self-explanatory Breast Diagnostic Capsules Network (2021)

Abstract: Building an interpretable AI diagnosis system for breast cancer is an important embodiment ofAI assisted medicine. Traditional breast cancer diagnosis methods based on machine learning areeasy 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 theAI 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 experimental results show that this breast diagnosis model improves the model performance and has good interpretability which is more suitable for clinical situations. IntroductionBreast 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 whichcan 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 anaccuracy of 91.11%. Wei (2009) proposed a classification method of breast cancer based on SVM andthe accuracy of the classifier experiment is 79.25%. These traditional AI diagnoses of breast tumors havelimited 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.

## 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 diagnosis of breast cancer is an important means. therefore, various diagnostic models were born. @xcite used support vector machine(svm) and naive bayes to classify morphological features with an accuracy of 91.11%. @xcite 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 diagnosis of breast tumor. @xcite put forward a kind of based on feature fusion with cnn deep features of breast computer-aided diagnosis methods, the accuracy is 92.3%. @xcite 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: fusion embedded for semantic vector: word2vec @xcite is a representative of the embedded word paradigm, but the word that produces word2vec is static, regardless of the relationship between context and connection. bert @xcite provides many pre-training models, which are excellent in various evaluation indexes. however, since bert is trained in open data sets, the word vectors directly extracted by bert are not accurate for specific fields. ## model bdcn is based on semantic embedding, multi-head attention and capsule network to realize text diagnosis of breast cancer examination report. the overall architecture is shown in figure 1 . it is mainly composed of three modules: the semantic segmentation layer of inspection report, the semantic tree knowledge embedding extraction word vector layer (sem-bert), and the capsule network assisted target feature multi-head attention representation classification layer (muti-cap). the input is an original mammography report. we use segmentation algorithm to attain preprocessing and segmentation of lesions and the implementation details are shown in section 3.1. then we propose sem-bert method, which uses semantic tree to improve bert and pre-training to obtain the word vector for the medical field. the details are discussed in section 3.2. the word embedding z 1 ,z 2 ,• • • ,z @xmath0,w 2 ,• • • ,w @xmath1. next, the feature capsule f c 1 ,f c 2 ,• • • ,f c @xmath2, @xmath3, 3] based on the dynamic routing algorithm, and the result capsule rc j , @xmath4, 3] is obtained by activating the network. finally, the benign and malignant diagnosis of breast tumors was predicted, and the implementation details are shown in section 3.3. ## experiment in this section, we mainly discuss the validity and interpretability of our model. firstly, the selection of methods in different stages is considered, such as the performance differences of sem-bert, bert and word2vec in acquiring word vectors. secondly, the model in this paper is compared with some unexplainable text classification models such as textcnn @xcite and lstm @xcite . finally, we demonstrate the interpretability of our model through experiments. ## conclusion we proposed a semantically embedded self-interpreted breast diagnostic capsule network model. semantic segmentation algorithm was used to segment the report, sem-bert method was used to obtain word vectors in medical field with hierarchical relationship, and capsule network with multiple attention was used to achieve prediction and classification of breast tumors. the validity of our model is better than other models in breast molybdenum target dataset. in addition, local self-interpretation method was used to provide intelligibility analysis, which was in line with doctors' clinical expectations. in the future, we will further study the global interpretability of the model and we hope to apply our technology to other diseases. c c l 2 0 2 1