Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports
Table 1: Main results for findings generation on the CX-CHR (upper) and IU-Xray (lower) datasets. BLEU-n denotes the BLEU score that uses up to n-grams.
['Dataset', 'Methods', 'BLEU-1', 'BLEU-2', 'BLEU-3', 'BLEU-4', 'ROUGE', 'CIDEr']
On both datasets, CMAS outperforms all baseline methods on almost all metrics, which indicates its overall efficacy for generating reports that resemble those written by human experts. The methods can be divided into two different groups: single sentence models (CNN-RNN, LRCN, AdaAtt, Att2in) and hierarchical models (CoAtt, HGRG-Agent, CMAS). Hierarchical models consistently outperform single sentence models on both datasets, suggesting that the hierarchical models are better for modeling paragraphs. The leading performances of CMAS-IL and CMAS-RL over the rest of hierarchical models demonstrate the validity of our practice in exploiting the structure information within sections.