Papers by Emily Tsai
Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards (2022.findings-emnlp)
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Jean-Benoit Delbrouck, Pierre Chambon, Christian Bluethgen, Emily Tsai, Omar Almusa, Curtis Langlotz
| Challenge: | Neural image-to-text radiology report generation systems have been successful on NLG metrics, but they are not factually complete or consistent due to inadequate training and evaluation. |
| Approach: | They propose a method to improve the factual completeness and correctness of generated radiology reports by using a dataset containing annotated chest X-ray images. |
| Outcome: | The proposed method significantly improves factual completeness and correctness of generated radiology reports on two open radiology report datasets. |
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. |
Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports (2020.acl-main)
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| Challenge: | Existing abstractive summarization models do not guarantee factual correctness of summaries . |
| Approach: | They propose a framework where models evaluate factual correctness by fact-checking it against its reference using an information extraction module. |
| Outcome: | The proposed method significantly improves the factual correctness and overall quality of outputs over a competitive neural summarization system, producing radiology summaries that approach the quality of human-authored ones. |