# Understanding Spatial Language in Radiology: Representation Framework, Annotation, and Spatial Relation Extraction from Chest X-ray Reports using Deep Learning

## TL;DR

This paper introduces Rad-SpRL, a framework for extracting detailed spatial information from radiology reports using deep learning, achieving high accuracy in identifying spatial roles in chest X-ray interpretations.

## Contribution

It presents a novel annotation scheme and a deep learning NLP method for extracting spatial roles from radiology reports, with significant performance results.

## Key findings

- Achieved F1 scores of 90.28 and 94.61 for Trajector and Landmark roles.
- Moderate performance for Diagnosis and Hedge roles with F1 scores around 71-73.
- Annotated 2000 reports to create a new corpus for spatial information extraction.

## Abstract

We define a representation framework for extracting spatial information from radiology reports (Rad-SpRL). We annotated a total of 2000 chest X-ray reports with 4 spatial roles corresponding to the common radiology entities. Our focus is on extracting detailed information of a radiologist's interpretation containing a radiographic finding, its anatomical location, corresponding probable diagnoses, as well as associated hedging terms. For this, we propose a deep learning-based natural language processing (NLP) method involving both word and character-level encodings. Specifically, we utilize a bidirectional long short-term memory (Bi-LSTM) conditional random field (CRF) model for extracting the spatial roles. The model achieved average F1 measures of 90.28 and 94.61 for extracting the Trajector and Landmark roles respectively whereas the performance was moderate for Diagnosis and Hedge roles with average F1 of 71.47 and 73.27 respectively. The corpus will soon be made available upon request.