Dataset Information

A dataset of chest X-ray reports annotated with Spatial Role Labeling annotations.

ABSTRACT: In this paper, we present a dataset consisting of 2000 chest X-ray reports (available as part of the Open-i image search platform) annotated with spatial information. The annotation is based on Spatial Role Labeling. The information includes annotating a radiographic finding, its associated anatomical location, any potential diagnosis described in connection to the spatial relation (between finding and location), and any hedging phrase used to describe the certainty level of a finding/diagnosis. All these annotations are identified with reference to a spatial expression (or Spatial Indicator) that triggers a spatial relation in a sentence. The spatial roles used to encode the spatial information are Trajector, Landmark, Diagnosis, and Hedge. In total, there are 1962 Spatial Indicators (mainly prepositions). There are 2293 Trajectors, 2167 Landmarks, 455 Diagnosis, and 388 Hedges in the dataset. This annotated dataset can be used for developing automatic approaches targeted toward spatial information extraction from radiology reports which then can be applied to numerous clinical applications. We utilize this dataset to develop deep learning-based methods for automatically extracting the Spatial Indicators as well as the associated spatial roles [1].

SUBMITTER: Datta S

PROVIDER: S-EPMC7451761 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Radiology reports contain a radiologist's interpretations of images, and these images frequently describe spatial relations. Important radiographic findings are mostly described in reference to an anatomical location through spatial prepositions. Such spatial relationships are also linked to various differential diagnoses and often described through uncertainty phrases. Structured representation of this clinically significant spatial information has the potential to be used in a variety of downstream clinical informatics applications. Our focus is to extract these spatial representations from the reports. For this, we first define a representation framework based on the Spatial Role Labeling (SpRL) scheme, which we refer to as Rad-SpRL. In Rad-SpRL, common radiological entities tied to spatial relations are encoded through four spatial roles: Trajector, Landmark, Diagnosis, and Hedge, all identified in relation to a spatial preposition (or Spatial Indicator). We annotated a total of 2,000 chest X-ray reports following Rad-SpRL. We then propose a deep learning-based natural language processing (NLP) method involving word and character-level encodings to first extract the Spatial Indicators followed by identifying the corresponding spatial roles. Specifically, we use a bidirectional long short-term memory (Bi-LSTM) conditional random field (CRF) neural network as the baseline model. Additionally, we incorporate contextualized word representations from pre-trained language models (BERT and XLNet) for extracting the spatial information. We evaluate both gold and predicted Spatial Indicators to extract the four types of spatial roles. The results are promising, with the highest average F1 measure for Spatial Indicator extraction being 91.29 (XLNet); the highest average overall F1 measure considering all the four spatial roles being 92.9 using gold Indicators (XLNet); and 85.6 using predicted Indicators (BERT pre-trained on MIMIC notes). The corpus is available in Mendeley at http://dx.doi.org/10.17632/yhb26hfz8n.1 and https://github.com/krobertslab/datasets/blob/master/Rad-SpRL.xml.

Dataset Information

A dataset of chest X-ray reports annotated with Spatial Role Labeling annotations.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets