Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation (2021.naacl-main)
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| Challenge: | Existing report generation systems suffer from incomplete and inconsistent generation, despite achieving high performance on natural language metrics such as CIDEr and BLEU. |
| Approach: | They propose two new rewards that encourage the generation of factually complete and consistent radiology reports by using an existing semantic equivalence metric. |
| Outcome: | The proposed system significantly improves the F1 score of a clinical information extraction performance on two open radiology report datasets. |
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