Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation (2021)

Abstract: Neural image-to-text radiology report generation systems offer the potential to improve radiology reporting by reducing the repetitive process of report drafting and identifying possible medical errors. However, existing report generation systems, despite achieving high performances on natural language generation metrics such as CIDEr or BLEU, still suffer from incomplete and inconsistent generations. Here we introduce two new simple rewards to encourage the generation of factually complete and consistent radiology reports: one that encourages the system to generate radiology domain entities consistent with the reference, and one that uses natural language inference to encourage these entities to be described in inferentially consistent ways. We combine these with the novel use of an existing semantic equivalence metric (BERTScore). We further propose a report generation system that optimizes these rewards via reinforcement learning. On two open radiology report datasets, our system substantially improved the F1 score of a clinical information extraction performance by +22.1 (Delta +63.9%). We further show via a human evaluation and a qualitative analysis that our system leads to generations that are more factually complete and consistent compared to the baselines.

## introduction an important new application of natural language generation (nlg) is to build assistive systems that take x-ray images of a patient and generate a textual report describing clinical observations in the images @xcite @xcite @xcite . figure 1 shows an example of a radiology report generated by such a system. this is a clinically important task, offering the potential to reduce radiologists' repetitive work and generally improve clinical communication @xcite . ## conclusion we proposed two new simple rewards and combined them with a semantic equivalence metric to improve image-to-text radiology report generation systems. the two new rewards make use of radiology domain entities extracted with a named entity recognizer and a weakly-supervised nli to capture the factual completeness and consistency of the generated reports. we further presented a transformer-based report generation system that directly optimizes these rewards with self-critical reinforcement learning. on two open datasets, we showed that our system generates reports that are more factually complete and consistent than the baselines and leads to reports with substantially higher scores in clinical metrics. the integration of entities and nli to improve the factual completeness and consistency of generation is not restricted to the domain of radiology reports, and we predict that a similar approach might similarly improve other data-to-text tasks.