Ling Wang

Chest X-ray report generation now defines its generation units directly as the anatomical region sets that statements are linked to in report annotations, rather than preset topics or single regions. RadCarve has a planner predict from the image which units exist and which regions each contains, then generates each unit's text independently from the joint visual evidence of that region set. On Chest ImaGenome, given region sets it improves CXRFEScore by 9.4% over strong baselines, and its end-to-end binding F1@0.5 is 0.451 versus 0.185 for RGRG and 0.089 for ChEX, with 0.761B inference parameters.

Chest X-ray report generation now defines its generation units directly as the anatomical region sets that statements are linked to in report annotations, rather than preset topics or single regions. RadCarve has a planner predict from the image which units exist and which regions each contains, then generates each unit's text independently from the joint visual evidence of that region set. On Chest ImaGenome, given region sets it improves CXRFEScore by 9.4% over strong baselines, and its end-to-end binding F1@0.5 is 0.451 versus 0.185 for RGRG and 0.089 for ChEX, with 0.761B inference parameters.