An Interactive Interpretability System for Breast Cancer Screening with Deep Learning

TL;DR

This paper introduces an interactive interpretability system that integrates deep learning models into radiologists' workflows for breast cancer screening, enhancing understanding and trust through user-driven explanations.

Contribution

It presents a domain-agnostic, interactive visual analytics system that improves model interpretability and user engagement with minimal additional labeling effort.

Findings

System effectively aids radiologists in understanding model decisions.

Progressive interaction refines explanations with minimal labeling.

Applicable to various medical imaging tasks.

Abstract

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.