An Interactive Interpretability System for Breast Cancer Screening with Deep Learning
This addresses the adoption barrier for medical AI in high-stakes decisions like breast cancer screening, though it is incremental as it builds on existing interpretability techniques.
The paper tackles the black-box nature of deep learning in breast cancer screening by proposing an interactive system that integrates interpretability techniques into radiologists' workflow, demonstrating it can provide finer-grained explainability with minimal labeling overhead.
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.