BDCN: Semantic Embedding Self-explanatory Breast Diagnostic Capsules Network

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 extraction 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.