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Despite recent medical advancements, breast cancer remains one of the mostprevalent and deadly diseases among women. Although machine learning-basedComputer-Aided Diagnosis (CAD) systems have shown potential to assistradiologists in analyzing medical images, the opaque nature of thebest-performing CAD systems has raised concerns about their trustworthiness andinterpretability. This paper proposes MT-BI-RADS, a novel explainable deeplearning approach for tumor detection in Breast Ultrasound (BUS) images. Theapproach offers three levels of explanations to enable radiologists tocomprehend the decision-making process in predicting tumor malignancy. Firstly,the proposed model outputs the BI-RADS categories used for BUS image analysisby radiologists. Secondly, the model employs multi-task learning toconcurrently segment regions in images that correspond to tumors. Thirdly, theproposed approach outputs quantified contributions of each BI-RADS descriptortoward predicting the benign or malignant class using post-hoc explanationswith Shapley Values.

Post-Hoc Explainability of BI-RADS Descriptors in a Multi-task Framework for Breast Cancer Detection and Segmentation