# Extracting Radiological Findings With Normalized Anatomical Information Using a Span-Based BERT Relation Extraction Model

arXiv: https://arxiv.org/abs/2108.09211 · HTML: https://mlsanity.com/paper/2108.09211/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 20 Aug 2021.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [Span-based Joint Entity and Relation Extraction with Transformer Pre-training](https://mlsanity.com/paper/1909.07755/) (2019)
- [BERT-based Ranking for Biomedical Entity Normalization](https://mlsanity.com/paper/1908.03548/) (2019)
- [Sequence Learning with RNNs for Medical Concept Normalization in User-Generated Texts](https://mlsanity.com/paper/1811.11523/) (2018)
- [RadLex Normalization in Radiology Reports](https://mlsanity.com/paper/2009.05128/) (2020)

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

- [A Novel Corpus of Annotated Medical Imaging Reports and Information Extraction Results Using BERT-based Language Models](https://mlsanity.com/paper/2403.18975/) (2024)
- [Generalizing through Forgetting -- Domain Generalization for Symptom Event Extraction in Clinical Notes](https://mlsanity.com/paper/2209.09485/) (2022)
- [Building blocks for complex tasks: Robust generative event extraction for radiology reports under domain shifts](https://mlsanity.com/paper/2306.09544/) (2023)

## Abstract (arXiv)

Medical imaging is critical to the diagnosis and treatment of numerous medical problems, including many forms of cancer. Medical imaging reports distill the findings and observations of radiologists, creating an unstructured textual representation of unstructured medical images. Large-scale use of this text-encoded information requires converting the unstructured text to a structured, semantic representation. We explore the extraction and normalization of anatomical information in radiology reports that is associated with radiological findings. We investigate this extraction and normalization task using a span-based relation extraction model that jointly extracts entities and relations using BERT. This work examines the factors that influence extraction and normalization performance, including the body part/organ system, frequency of occurrence, span length, and span diversity. It discusses approaches for improving performance and creating high-quality semantic representations of radiological phenomena.