Toward Expanding the Scope of Radiology Report Summarization to Multiple Anatomies and Modalities (2023)

Abstract: 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.

## introduction a radiology report is a document that provides information about the results of a radiology study. it usually includes a findings section with key observations from the study and an impression section with the radiologist's overall conclusions. the latter is the most critical part of the report and is typically based on both the findings and the patient's condition. it can be helpful to automate the process of generating the impression section because it can be time-consuming and prone to errors when done manually @xcite . recently, substantial progress has been made towards research on automated radiology report summarization (rrs) @xcite @xcite . however, the field of rrs faces several key limitations. first, the experimental results of many * equal contribution. ## dataset construction in this section, we present the new mimic-rrs dataset designed for radiology report summarization across multiple modalities and anatomies. comparisons with existing datasets are shown in ## algorithmic analysis in this section, we conduct experiments to analyze the performance of different models on mimic-rrs. we provide three categories of analyses: inmodality-anatomy, cross-modality-anatomy, and clinical efficacy. ## conclusion and discussion in this paper, we highlight and address several weaknesses associated with the radiology report summarization task. first, from a data perspective, we propose a publicly available dataset named mimic-rrs involving data samples from twelve modality-anatomy pairs, with 79,779 samples from mimic-iii and 128,003 samples from mimic-cxr. second, we conducted more than 40 experiments and over 400 cross-modality-anatomy evaluations to benchmark the performance of different models. we show that instead of designing complicated modules, we can start from an appropriate backbone model such as bart. finally, we proposed an elegant and simple metric, f1-radgraph, to evaluate the factual correctness of summaries generated for any modality and anatomy. in the future, we hope that our work broadens the scope of the radiology report summarization task and contributes to the development of reliable rrs models that generalize well to new anatomies and modalities.