  Detecting and classifying lesions in breast ultrasound images is a promising
application of artificial intelligence (AI) for reducing the burden of cancer
in regions with limited access to mammography. Such AI systems are more likely
to be useful in a clinical setting if their predictions can be explained to a
radiologist. This work proposes an explainable AI model that provides
interpretable predictions using a standard lexicon from the American College of
Radiology's Breast Imaging and Reporting Data System (BI-RADS). The model is a
deep neural network featuring a concept bottleneck layer in which known BI-RADS
features are predicted before making a final cancer classification. This
enables radiologists to easily review the predictions of the AI system and
potentially fix errors in real time by modifying the concept predictions. In
experiments, a model is developed on 8,854 images from 994 women with expert
annotations and histological cancer labels. The model outperforms
state-of-the-art lesion detection frameworks with 48.9 average precision on the
held-out testing set, and for cancer classification, concept intervention is
shown to increase performance from 0.876 to 0.885 area under the receiver
operating characteristic curve. Training and evaluation code is available at
https://github.com/hawaii-ai/bus-cbm.
