Yingshu Li

Iterative refinement in radiology report generation can significantly enhance accuracy, with DRRG outperforming traditional methods despite using a smaller model.

Structuring supervision around disease-specific relationships leads to better radiology report generation than simply scaling up model size.

High-resolution chest X-ray perception can be achieved without inflating token counts, leading to better diagnostic accuracy and report generation.

Radiology report generation models often hide clinically superior reports within their candidate pools, and a simple consensus-based selection method can unlock them.