Abstract
Medical imaging is critical to the diagnosis, surveillance, and treatment of many health conditions, including oncological, neurological, cardiovascular, and musculoskeletal disorders, among others. Radiologists interpret these complex, unstructured images and articulate their assessments through narrative reports that remain largely unstructured. This unstructured narrative must be converted into a structured semantic representation to facilitate secondary applications such as retrospective analyses or clinical decision support. Here, we introduce the Corpus of Annotated Medical Imaging Reports (CAMIR), which includes 609 annotated radiology reports from three imaging modality types: Computed Tomography, Magnetic Resonance Imaging, and Positron Emission Tomography-Computed Tomography. Reports were annotated using an event-based schema that captures clinical indications, lesions, and medical problems. Each event consists of a trigger and multiple arguments, and a majority of the argument types, including anatomy, normalize the spans to pre-defined concepts to facilitate secondary use. CAMIR uniquely combines a granular event structure and concept normalization. To extract CAMIR events, we explored two BERT (Bi-directional Encoder Representation from Transformers)-based architectures, including an existing architecture (mSpERT) that jointly extracts all event information and a multi-step approach (PL-Marker++) that we augmented for the CAMIR schema.
Abstract (translated)
医学影像对于许多疾病的诊断、监测和治疗至关重要，包括癌症、神经系统疾病、心血管疾病和骨骼肌骨骼疾病等。放射科医生解释这些复杂且无结构的图像，并通过非结构化的叙述性报告来阐述他们的评估。这些非结构化的叙述必须转换为结构化的语义表示，以促进后期的应用，如回顾性分析或临床决策支持。在这里，我们介绍了名为“标记医学影像报告集”（CAMIR）的文献集，其中包括3种成像模式类型：计算机断层扫描（CT）、磁共振成像（MRI）和正电子发射断层扫描-计算机断层扫描（PET-CT）的609篇注释过的报告。这些报告使用事件基于的 schema 进行注释，捕捉临床指征、病变和医疗问题。每个事件由触发器和多个论据组成，而且大多数论据类型，包括解剖学，正常化跨度以促进二次使用。CAMIR 独特地结合了粒度事件结构和概念正常化。为了提取 CAMIR 事件，我们探索了两种基于 BERT（双向编码器表示从 transformer）的架构，包括现有的架构（mSpERT）和新方法（PL-Marker++），我们对其进行了增强以适应 CAMIR 模式。
URL
https://arxiv.org/abs/2403.18975
