2022

Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards

Delbrouck, Jean-Benoit, Chambon, Pierre, Bluethgen, Christian et al.

Understand

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.

These systems have achieved promising performance as measured by widely used NLG metrics such as BLEU and CIDEr.
However, the current systems face important limitations.
First, they present an increased complexity in architecture that offers only marginal improvements on NLG metrics.

Built on

MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs
Original
Alistair E. W. Johnson, Tom J. Pollard, Nathaniel R. Greenbaum, Matthew P. Lungren, Chih-ying Deng, Yifan Peng, Zhiyong Lu, Roger G. Mark, Seth J. Berkowitz, and Steven Horng. 2019 · 1901
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