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：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，但往往耗时且易出错。结构化报告作为一种缓解方案，不仅能节省时间，还可比自由文本报告实现更精准的评估。然而，目前关于自动化结构化报告的研究有限，且缺乏公开基准来评估和比较不同方法。为填补这一空白，我们提出了Rad-ReStruct——一个以X光图像结构化报告形式提供细粒度、层次化注释的新基准数据集。我们将结构化报告任务建模为分层视觉问答（VQA），并提出hi-VQA这一新颖方法——通过考虑既往问答形式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上达到了与现有最优方法相匹敌的性能，同时成为无领域专用视觉-语言预训练方法中的最佳方案，并在Rad-ReStruct上建立了强有力的基线。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，也为该领域的未来研究提供了首个宝贵基准。我们将于论文接收后公开发布所有注释、注释生成代码、模型评估代码及训练代码。数据集与代码可通过https://github.com/ChantalMP/Rad-ReStruct获取。