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Table 1: Automatic evaluations on IU dataset. * results from li2018hybrid. + results from xue2018multimodal.
['Model', 'CIDEr', 'ROUGE', 'BLEU-1', 'BLEU-2', 'BLEU-3', 'BLEU-4']
Evaluation of Automatic Medical Image Reports. We use the metrics for NLP tasks such as BLEU papineni2002bleu, ROUGE lin2004rouge, and CIDEr Agrawal:2017:VVQ:3088990.3089103 for automatic performance evaluation. While BLEU scores measure the percentage of consistency between the automatic report and the manual report in light of the automatic report (precision), it is not illuminative in assessing the amount of information captured in the automatic report in light of the manual report (recall). In real-world clinical applications, both recall and precision are critical in evaluating the quality of an automatic report.