Toward expanding the scope of radiology report summarization to multiple anatomies and modalities
This work addresses the problem of limited scope and reproducibility in RRS for medical researchers and practitioners, but it is incremental as it builds on existing datasets and methods.
The authors tackled the limitations of radiology report summarization (RRS) by creating a new dataset (MIMIC-RRS) that expands to multiple anatomies and modalities, and they evaluated models on this dataset, achieving performance improvements with a RadGraph metric showing factual correctness gains.
Radiology report summarization (RRS) is a growing area of research. Given the Findings section of a radiology report, the goal is to generate a summary (called an Impression section) that highlights the key observations and conclusions of the radiology study. However, RRS currently faces essential limitations.First, many prior studies conduct experiments on private datasets, preventing reproduction of results and fair comparisons across different systems and solutions. Second, most prior approaches are evaluated solely on chest X-rays. To address these limitations, we propose a dataset (MIMIC-RRS) involving three new modalities and seven new anatomies based on the MIMIC-III and MIMIC-CXR datasets. We then conduct extensive experiments to evaluate the performance of models both within and across modality-anatomy pairs in MIMIC-RRS. In addition, we evaluate their clinical efficacy via RadGraph, a factual correctness metric.