A chest X-ray report quality metric that combines fine-grained text finding matching with anatomical grounding on images is proposed and shown to be more sensitive to factual errors than BLEU, BERTScore, and RadGraph.
A survey on au- tomatic generation of medical imaging reports based on deep learning,
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Evaluating Automated Radiology Report Quality through Fine-Grained Phrasal Grounding of Clinical Findings
A chest X-ray report quality metric that combines fine-grained text finding matching with anatomical grounding on images is proposed and shown to be more sensitive to factual errors than BLEU, BERTScore, and RadGraph.