Producing radiologist-quality reports for interpretable artificial intelligence

TL;DR

This paper introduces a language-based interpretability method for deep learning in medical imaging, producing descriptive sentences that improve understanding and trust compared to traditional saliency maps.

Contribution

It presents a novel, model-agnostic approach that generates natural language explanations for medical decisions, enhancing interpretability without extensive additional labeling.

Findings

Generated sentences contain key decision information

Doctors prefer text explanations over saliency maps

Combining text and visualizations improves interpretability

Abstract

Current approaches to explaining the decisions of deep learning systems for medical tasks have focused on visualising the elements that have contributed to each decision. We argue that such approaches are not enough to "open the black box" of medical decision making systems because they are missing a key component that has been used as a standard communication tool between doctors for centuries: language. We propose a model-agnostic interpretability method that involves training a simple recurrent neural network model to produce descriptive sentences to clarify the decision of deep learning classifiers. We test our method on the task of detecting hip fractures from frontal pelvic x-rays. This process requires minimal additional labelling despite producing text containing elements that the original deep learning classification model was not specifically trained to detect. The experimental results show that: 1) the sentences produced by our method consistently contain the desired information, 2) the generated sentences are preferred by doctors compared to current tools that create saliency maps, and 3) the combination of visualisations and generated text is better than either alone.