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Deep learning methods, in particular convolutional neural networks, haveemerged as a powerful tool in medical image computing tasks. While thesecomplex models provide excellent performance, their black-box nature may hinderreal-world adoption in high-stakes decision-making. In this paper, we proposean interactive system to take advantage of state-of-the-art interpretabilitytechniques to assist radiologists with breast cancer screening. Our systemintegrates a deep learning model into the radiologists' workflow and providesnovel interactions to promote understanding of the model's decision-makingprocess. Moreover, we demonstrate that our system can take advantage of userinteractions progressively to provide finer-grained explainability reports withlittle labeling overhead. Due to the generic nature of the adoptedinterpretability technique, our system is domain-agnostic and can be used formany different medical image computing tasks, presenting a novel perspective onhow we can leverage visual analytics to transform originally staticinterpretability techniques to augment human decision making and promote theadoption of medical AI.

An Interactive Interpretability System for Breast Cancer Screening with Deep Learning