论文标题
深度学习的乳腺癌筛查的交互式解释性系统
An Interactive Interpretability System for Breast Cancer Screening with Deep Learning
论文作者
论文摘要
深度学习方法，尤其是卷积神经网络，已成为医学图像计算任务中的强大工具。尽管这些复杂的模型提供了出色的性能，但它们的黑盒性质可能会阻碍现实世界中的现实决策。在本文中，我们提出了一个交互式系统，以利用最新的可解释性技术来帮助放射科医生进行乳腺癌筛查。我们的系统将深度学习模型集成到放射学家的工作流程中，并提供新颖的互动，以促进对模型决策过程的理解。此外，我们证明我们的系统可以逐步利用用户交互，以提供更细粒度的可解释性报告，而在开销的情况下很少。由于采用的可解释性技术的通用性质，我们的系统是域形的，可用于许多不同的医学图像计算任务，展示了我们如何利用视觉分析来转变最初静态的可解释性技术来增强人类决策制定并促进医疗AI的采用。
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.
