Yasuhide Miura — accepted AI-conference papers

2022

Factual Accuracy is not Enough: Planning Consistent Description Order for Radiology Report Generation

Toru Nishino, Yasuhide Miura, Tomoki Taniguchi, Tomoko Ohkuma, Yuki Suzuki, Shoji Kido +1

EMNLP 2022main

Radiology report generation systems have the potential to reduce the workload of radiologists by automatically describing the findings in medical images.To broaden the application of the report generation system, the system should generate reports that are not only factually accurate but also chrono…

Cited by 10 Source ↗PDF ↗code ↗Scholar ↗

2021

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

Yasuhide Miura, Yuhao Zhang, Emily Tsai, Curtis Langlotz, Dan Jurafsky

NAACL 2021long

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 langu…

Cited by 195 Source ↗PDF ↗code ↗Scholar ↗