  In healthcare, it is essential to explain the decision-making process of
machine learning models to establish the trustworthiness of clinicians. This
paper introduces BI-RADS-Net, a novel explainable deep learning approach for
cancer detection in breast ultrasound images. The proposed approach
incorporates tasks for explaining and classifying breast tumors, by learning
feature representations relevant to clinical diagnosis. Explanations of the
predictions (benign or malignant) are provided in terms of morphological
features that are used by clinicians for diagnosis and reporting in medical
practice. The employed features include the BI-RADS descriptors of shape,
orientation, margin, echo pattern, and posterior features. Additionally, our
approach predicts the likelihood of malignancy of the findings, which relates
to the BI-RADS assessment category reported by clinicians. Experimental
validation on a dataset consisting of 1,192 images indicates improved model
accuracy, supported by explanations in clinical terms using the BI-RADS
lexicon.
