利用协作知识生成胸部X光片的放射学发现
Generation of Radiology Findings in Chest X-Ray by Leveraging Collaborative Knowledge
AI总结：
本文提出一种两步方法，先检测胸部X光片中的异常区域，再利用大语言模型生成相关放射学发现，以减轻放射科医生负担并增强可解释性。
摘要：
在典型放射学报告的所有子部分中，临床指征、发现和印象通常反映患者健康状况的重要细节。印象中包含的信息也常常在发现中有所涵盖。虽然发现和印象可以通过检查图像来推断，但临床指征通常需要额外的上下文。解释医学图像的认知任务仍然是放射学工作流程中最关键且通常最耗时的步骤。本文不生成端到端的放射学报告，而是专注于从医学图像的自动解释中生成发现，特别是胸部X光片（CXR）。因此，这项工作旨在减少放射科医生的负担，他们大部分时间都在撰写或口述发现。与以往将放射学报告生成视为单步图像字幕任务的研究不同，我们进一步考虑了CXR图像解释的复杂性，并提出了一种两步方法：（a）检测图像中的异常区域，以及（b）通过使用生成式大语言模型（LLM）为异常区域生成相关文本。这种两步方法引入了一层可解释性，并使框架与放射科医生在审查CXR时使用的系统推理相一致。
英文摘要：
Among all the sub-sections in a typical radiology report, the Clinical Indications, Findings, and Impression often reflect important details about the health status of a patient. The information included in Impression is also often covered in Findings. While Findings and Impression can be deduced by inspecting the image, Clinical Indications often require additional context. The cognitive task of interpreting medical images remains the most critical and often time-consuming step in the radiology workflow. Instead of generating an end-to-end radiology report, in this paper, we focus on generating the Findings from automated interpretation of medical images, specifically chest X-rays (CXRs). Thus, this work focuses on reducing the workload of radiologists who spend most of their time either writing or narrating the Findings. Unlike past research, which addresses radiology report generation as a single-step image captioning task, we have further taken into consideration the complexity of interpreting CXR images and propose a two-step approach: (a) detecting the regions with abnormalities in the image, and (b) generating relevant text for regions with abnormalities by employing a generative large language model (LLM). This two-step approach introduces a layer of interpretability and aligns the framework with the systematic reasoning that radiologists use when reviewing a CXR.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
