# A Novel Corpus of Annotated Medical Imaging Reports and Information Extraction Results Using BERT-based Language Models

arXiv: https://arxiv.org/abs/2403.18975 · HTML: https://mlsanity.com/paper/2403.18975/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 42 references that are in the map.
- First posted on arXiv 27 Mar 2024.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [RadGraph: Extracting Clinical Entities and Relations from Radiology Reports](https://mlsanity.com/paper/2106.14463/) (2021)
- [Span-based Joint Entity and Relation Extraction with Transformer Pre-training](https://mlsanity.com/paper/1909.07755/) (2019)
- [Clinical Concept Extraction with Contextual Word Embedding](https://mlsanity.com/paper/1810.10566/) (2018)
- [Understanding Spatial Language in Radiology: Representation Framework, Annotation, and Spatial Relation Extraction from Chest X-ray Reports using Deep Learning](https://mlsanity.com/paper/1908.04485/) (2019)
- [Transferability of Neural Network Clinical De-identification Systems](https://mlsanity.com/paper/2102.08517/) (2021)
- [RadLex Normalization in Radiology Reports](https://mlsanity.com/paper/2009.05128/) (2020)
- [Extracting Radiological Findings With Normalized Anatomical Information Using a Span-Based BERT Relation Extraction Model](https://mlsanity.com/paper/2108.09211/) (2021)
- [Event-based clinical findings extraction from radiology reports with pre-trained language model](https://mlsanity.com/paper/2112.13512/) (2021)

## Built on by (most cited first, 3 of 3)

- [Event Extraction in Large Language Model](https://mlsanity.com/paper/2512.19537/) (2025)
- [CACER: Clinical Concept Annotations for Cancer Events and Relations](https://mlsanity.com/paper/2409.03905/) (2024)
- [Automated Identification of Incidentalomas Requiring Follow-Up: A Multi-Anatomy Evaluation of LLM-Based and Supervised Approaches](https://mlsanity.com/paper/2512.05537/) (2025)

## Abstract (arXiv)

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.