Learning a Clinically-Relevant Concept Bottleneck for Lesion Detection in Breast Ultrasound

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. 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 which predicts BI-RADS features in a concept bottleneck layer for cancer classification. This architecture enables radiologists to interpret the predictions of the AI system from the concepts 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. For cancer classification concept intervention increases 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 .