Radiology reporting is a crucial part of the communication between radiologists and other medical professionals, but it can be time-consuming and error-prone. One approach to alleviate this is structured reporting, which saves time and enables a more accurate evaluation than free-text reports. However, there is limited research on automating structured reporting, and no public benchmark is available for evaluating and comparing different methods. To close this gap, we introduce Rad-ReStruct, a new benchmark dataset that provides fine-grained, hierarchically ordered annotations in the form of structured reports for X-Ray images. We model the structured reporting task as hierarchical visual question answering (VQA) and propose hi-VQA, a novel method that considers prior context in the form of previously asked questions and answers for populating a structured radiology report. Our experiments show that hi-VQA achieves competitive performance to the state-of-the-art on the medical VQA benchmark VQARad while performing best among methods without domain-specific vision-language pretraining and provides a strong baseline on Rad-ReStruct. Our work represents a significant step towards the automated population of structured radiology reports and provides a valuable first benchmark for future research in this area. We will make all annotations and our code for annotation generation, model evaluation, and training publicly available upon acceptance. Our dataset and code is available at https://github.com/ChantalMP/Rad-ReStruct.

翻译：摘要：放射报告是放射科医生与其他医疗专业人员沟通的关键环节，但该过程可能耗时且易出错。结构化报告作为一种缓解方案，能节省时间并比自由文本报告实现更准确的评估。然而，目前关于自动化结构化报告的研究有限，且缺乏用于评估和比较不同方法的公开基准。为填补这一空白，我们引入了Rad-ReStruct——一个新型基准数据集，其以结构化报告形式为X光图像提供细粒度、层次化有序的标注。我们将结构化报告任务建模为层次化视觉问答（VQA），并提出hi-VQA方法，该方法通过整合先前问答的上下文信息来填充结构化放射报告。实验表明，hi-VQA在医学VQA基准VQARad上取得了与现有最优方法竞争的性能，同时在没有领域特定视觉-语言预训练的方法中表现最佳，并为Rad-ReStruct提供了强基线。本研究标志着向自动化填充结构化放射报告迈出的重要一步，并为该领域的未来研究提供了首个有价值的基准。论文接收后，我们将公开所有标注、标注生成代码、模型评估及训练代码。数据集与代码地址为：https://github.com/ChantalMP/Rad-ReStruct。