BI-RADS-NET-V2: A Composite Multi-Task Neural Network for Computer-Aided Diagnosis of Breast Cancer in Ultrasound Images With Semantic and Quantitative Explanations

Computer-aided Diagnosis (CADx) based on explainable artificial intelligence (XAI) can gain the trust of radiologists and effectively improve diagnosis accuracy and consultation efficiency. This paper proposes BI-RADS-Net-V2, a novel machine learning approach for fully automatic breast cancer diagnosis in ultrasound images. The BI-RADS-Net-V2 can accurately distinguish malignant tumors from benign ones and provides both semantic and quantitative explanations. The explanations are provided in terms of clinically proven morphological features used by clinicians for diagnosis and reporting mass findings, i.e., Breast Imaging Reporting and Data System (BI-RADS). The experiments on 1,192 Breast Ultrasound (BUS) images indicate that the proposed method improves the diagnosis accuracy by taking full advantage of the medical knowledge in BI-RADS while providing both semantic and quantitative explanations for the decision.

Automated summary

This paper proposes a new machine learning method called BI-RADS-Net-V2 for breast cancer diagnosis using explainable artificial intelligence (XAI). The method can accurately distinguish malignant tumors from benign ones and provides clinically proven morphological features used for diagnosis in the Breast Imaging Reporting and Data System (BI-RADS). Experimental results on 1,192 Breast Ultrasound images demonstrate that the proposed method improves diagnostic accuracy by leveraging medical knowledge in BI-RADS and providing both semantic and quantitative explanations.