  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.
