  Deep learning methods, in particular convolutional neural networks, have
emerged as a powerful tool in medical image computing tasks. While these
complex models provide excellent performance, their black-box nature may hinder
real-world adoption in high-stakes decision-making. In this paper, we propose
an interactive system to take advantage of state-of-the-art interpretability
techniques to assist radiologists with breast cancer screening. Our system
integrates a deep learning model into the radiologists' workflow and provides
novel interactions to promote understanding of the model's decision-making
process. Moreover, we demonstrate that our system can take advantage of user
interactions progressively to provide finer-grained explainability reports with
little labeling overhead. Due to the generic nature of the adopted
interpretability technique, our system is domain-agnostic and can be used for
many different medical image computing tasks, presenting a novel perspective on
how we can leverage visual analytics to transform originally static
interpretability techniques to augment human decision making and promote the
adoption of medical AI.
