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

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arxiv 2010.10042 v2 pith:MB5MDHDZ submitted 2020-10-20 cs.CL

Yasuhide Miura , Yuhao Zhang , Emily Bao Tsai , Curtis P. Langlotz , Dan Jurafsky This is my paper
classification cs.CL
keywords generationradiologyreportconsistentsystemcompleteencourageentities
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 5 Pith papers

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  5. Enhancing Biomedical Multi-modal Representation Learning with Multi-scale Pre-training and Perturbed Report Discrimination

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